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Small, David L. "Information content of polarimetric synthetic aperture radar data." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30103.
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Research into the analysis of polarimetric synthetic aperture radar (SAR) data continues to reveal new applications and data extraction techniques. The objective of this thesis is to examine the information content of a quad-polarization SAR, and determine which polarimetric variables are most useful for classification purposes.
The four complex polarimetric radar channels (HH, HV, VH, and VV) are expressed as nine scattering matrix cross-product "features" (with the loss of only absolute phase), and the relative utility of each for terrain classification is examined. Feature utility is examined in two ways — by measuring how each feature separates classes of terrain in an image, and by measuring how well a classifier performs with and without each feature. The features are then ranked in order of utility to the classifier, or in order of information content.
A sharp distinction is found between those features that provide information useful to the classifier, and those that do not. It is found that those features that are defined as the product of a co-polarized and a cross-polarized term can be relatively safely ignored, with little loss of classification accuracy. This would be useful for reducing data transmission, storage, and processing requirements, and for designing future simplified radar systems. There is qualitative evidence that classification performance can actually be improved when these features are ignored.
Of three simplified radar systems considered, the co-polarized design (returning only the complex HH and VV channels) in general produced classifications closest to that of a fully polarimetric SAR.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Brown, Sarah Caroline Mellows. "High resolution polarimetric imaging of biophysical objects using synthetic aperture radar." Thesis, University of Sheffield, 1998. http://etheses.whiterose.ac.uk/10223/.
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A synthetic aperture microwave near-field system is used to image biophysical objects in order to investigate the nature of radar-target interaction. Two different imaging algorithms for focusing data collected over a two-dimensional planar aperture are investigated. The first of these is the single frequency backward propagation technique which is mathematically simple to implement and provides a high degree of resolution. Secondly, a multifrequency development of the backward propagation algorithm is presented and derived from two separate perspectives. This latter algorithm, known as the auto-focusing algorithm, requires no information about the range of the target from the aperture. Full characterisation by simulation of both algorithms is carried out and different filtering techniques are investigated. The backward propagation algorithm is applied to the polarimetric imaging of three different leafless trees and a sugar beet plant at the X-band frequency of 10GHz. The images so produced demonstrate that the backscattered signal is dependent on the orientation of individual tree elements with respect to the polarisation. Furthermore, multiple scattering terms can be identified within the structure of the tree. The auto-focusing algorithm is applied to the polarimetric imaging of two trees at 10GHz and repeat measurements are made over several months. As with the single frequency measurements, the backscattered signal is dependent on the orientation of individual tree elements relative to the polarisation. The relative contributions from the leaves and branches of the trees to the backscattered signal are assessed and found to be seasonally dependent. Measurements are also carried out to investigate the variation of backscatter from a beech tree with varying incidence angle. It is demonstrated that at small angles of incidence, the leaves are the dominant source of backscatter but at large incidence angles, the branches and trunk of the tree have the greatest contrbution.
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Danklmayer, Andreas. "Propagation effects and polarimetric methods in synthetic aperture radar imaging : 15 Tabellen /." Köln : DLR, Bibliotheks- und Informationswesen, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016768338&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Showman, Gregory Alan. "Polarimetric calibration of ultra-wideband SAR imagery." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/13368.
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Khan, Salman Saeed. "Non-gaussian multivariate probability models and parameter estimation for polarimetric synthetic aperture radar data." Thesis, University of Surrey, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.600036.
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The inadequacy of gaussian statistics in describing certain regions of a synthetic aperture radar (SAR) image can be explained by the violation of fundamental gaussian assumptions due to the increase in spatial resolution and target heterogeneity. Many non-gaussian probability models, competing in modeling flexibility, mathematical tractability, and simplicity / accuracy of parameter estimation, have been proposed in the last two decades to model single-channel and polarimetric SAR (PoISAR) data. This thesis explores the flexible polarimetric G distribution, which has many other nongaussian probability models as its special forms. Previously, it has not been applied to PolSAR data primarily because of its relatively complicated probability density function (pdf). But recently, other flexible distributions, e.g. Kummer-U distribution, with similarly complicated pdfs have been successfully applied to PolSAR data. Therefore, it is expected that the application of G distribution) along with the proposal of its new, accurate) and efficient parameter estimators) to model PolSAR data will bring significant contributions to the field. Firstly, singlelook version of polarimetric G distribution is derived. Then) several new parameter estimators for this distribution are proposed. The performance of these estimators are compared to each other on simulated PolSAR data. One of the better performing estimators results from the novel analysis of G distribution using Mellin kind statistics. However, this estimator does not have closed form expressions, which is an undesirable property. A new framework for parameter estimation, based on fractional moments of multilook polarimetric whitening filter, is thus proposed. It results in simple, accurate, and computationally inexpensive estimators for all the well known non-gaussian probability models including the 9 distribution. On real PolSAR data) the fitting accuracy of 9 distribution, bundled with its new estimators, is compared with some other competitive non-gaussian models. It is found that the proposed distribution adequately fits PolSAR data significantly better than its special cases, and very similar to the Kummer-U distribution. However, the software implementation of 9 distribution pdf is observed to be relatively more stable than the Kummer-U distribution pdf.
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Marino, Armando. "New target detector based on geometrical perturbation filters for polarimetric Synthetic Aperture Radar (POL-SAR)." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4891.
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Synthetic Aperture Radar (SAR) is an active microwave remote sensing system able to acquire high resolution images of the scattering behaviour of an observed scene. The contribution of SAR polarimetry (POLSAR) in detection and classification of objects is described and found to add valuable information compared to previous approaches. In this thesis, a new target detection/classification methodology is developed that makes novel use of the polarimetric information of the backscattered field from a target. The detector is based on a geometrical perturbation filter which correlates the target of interest with its perturbed version. Specifically, the operation is accomplished with a polarimetric coherence representing a weighted and normalised inner product between the target and its perturbed version, where the weights are extracted from the observables. The mathematical formulation is general and can be applied to any deterministic (point) target. However, in this thesis the detection is primarily focused on multiple reflections and oriented dipoles due to their extensive availability in common scenarios. An extensive validation against real data is provided exploiting different datasets. They include one airborne system: E-SAR L-band (DLR, German Aerospace Centre); and three satellite systems: ALOS-PALSAR L-band (JAXA, Japanese Aerospace Exploration Agency), RADARSAT-2 C-band (Canadian Space Agency) and TerraSAR-X X-band (DLR). The attained detection masks reveal significant agreement with the expected results based on the theoretical description. Additionally, a comparison with another widely used detector, the Polarimetric Whitening Filter (PWF) is presented. The methodology proposed in this thesis appears to outperform the PWF in two significant ways: 1) the detector is based on the polarimetric information rather than the amplitude of the return, hence the detection is not restricted to bright targets; 2) the algorithm is able to discriminate among the detected targets (i.e. target recognition).
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Zhang, Xiaohu, and 张啸虎. "Automatic detection of land cover changes using multi-temporal polarimetric SAR imagery." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193496.
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Dramatic land-cover changes have occurred in a broad range of spatial and temporal scales over the last decades. Satellite remote sensing, which can observe the earth's surface in a consistent manner, has been playing an important role in monitoring and evaluating land-cover changes. Meanwhile, optical remote sensing, a common approach to acquiring land-cover information, is limited by weather conditions and thus is greatly constrained in areas with frequent cloud cover and rainfall. Recent advances in polarimetric SAR (PolSAR) provide a promising means to extract timely information of land-cover changes regardless of weather conditions.
SAR satellite can pass through an area from different orbits, namely ascending orbit and descending orbit. The PolSAR images from the same orbit will have similar backscattering even with different incident angles. But if images are acquired from different orbits, the backscattering will vary greatly, which causes many difficulties to land cover change detection. The proposed algorithms in this study can perform land cover change detection in three situations: 1) repeat-pass images (image from the same orbit and with same incident angle, 2) images from the same orbit but with different incident angle, and 3) images from different orbits. Using images from different orbits will largely reduce the monitoring interval which is important in the surveillance of natural disasters.
The present study proposes 1) a sub-pixel automatic registration technique, 2) an automatic change detection technique and 3) an iterative framework to process a time series of PolSAR images that can be applied to the PolSAR images from different orbits. Firstly, automatic registration is crucial to the change detection task because a small positional error will largely degrade the accuracy of change detection. The automatic registration technique is based on the multi-scale Harris corner detector. To improve the efficiency and robustness, the orientation angle differencing method is proposed to reject outliers. This differencing method has been proved effective even in the experiment of using PolSAR images from different orbits when less than 5% of the feature point matches are correct. Secondly, the change detection technique can automatically detect land-cover conversions and classify the newly input image. Hierarchical segmentation has been applied in the change detection which generates objects within the constraint of the previous classification map. Multivariate kernel density estimation is applied to classify newly input PolSAR image. The experiments show that the proposed change detection technique can mitigate the effect of polarimetric orientation shift when the PolSAR images are from different orbits, and it can achieve high accuracy even when complex local deformation is appeared. Lastly, the iterative framework, which integrates the automatic registration and automatic change detection techniques, is proposed to process a time series of PolSAR images. In the iterative process, no obvious decrease of classification accuracy is observed. Therefore, the proposed framework provides a potential treatment to derive land-cover dynamics from a time series of PolSAR images from different orbits.published_or_final_version
Urban Planning and Design
Doctoral
Doctor of Philosophy
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He, Wenju [Verfasser], and Olaf [Akademischer Betreuer] Hellwich. "Segmentation-Based Building Analysis from Polarimetric Synthetic Aperture Radar Images / Wenju He. Betreuer: Olaf Hellwich." Berlin : Universitätsbibliothek der Technischen Universität Berlin, 2011. http://d-nb.info/1014971683/34.
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Black, James Noel. "Development of a Support-Vector-Machine-based Supervised Learning Algorithm for Land Cover Classification Using Polarimetric SAR Imagery." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85391.
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Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarimetric data is to decompose the data into the underlying scattering mechanisms. In this research, the Freeman and Durden scattering model is applied to ALOS PALSAR fully polarimetric data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the Freeman and Durden work, the classification capability of the model is assessed on amazon rainforest land cover types using a supervised Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined. Additionally, the performance of the median as a robust estimator in noisy environments for multi-pixel windowing is also characterized.Master of Science
Land type classification using Radar data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of Radar data. One fundamental step in any classification algorithm is the selection and/or extraction of discriminating features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarized Radar data is to decompose the data into the underlying scatter components. In this research, a scattering model is applied to real world data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the scattering model, the classification capability of the model is assessed on amazon rainforest land types using a Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined and compared using different estimators.
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Dilsavor, Ronald L. "Detection of target scattering centers in terrain clutter using an ultra-wideband, fully-polarimetric synthetic aperture radar /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487847761306763.
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Mestre-Quereda, Alejandro. "Advanced Processing Techniques and Applications of Synthetic Aperture Radar Interferometry." Doctoral thesis, Universidad de Alicante, 2019. http://hdl.handle.net/10045/101167.
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Synthetic Aperture Radar interferometry (InSAR) is a powerful and established technique, which is based on exploiting the phase difference between pairs of SAR images, and which aims to measure changes in the Earth’s surface. The quality of the interferometric phase is therefore the most crucial factor for deriving reliable products by means of this technique. Unfortunately, the quality of the phase is often degraded due to multiple decorrelation factors, such as the geometrical or temporal decorrelation. Accordingly, central to this PhD thesis is the development of advanced processing techniques and algorithms to extensively reduce such disturbing effects caused by decorrelation. These new techniques include an improved range spectral filter which fully utilizes an external Digital Elevation Model (DEM) to reduce geometrical decorrelation between pairs of SAR images, especially in areas strongly influenced by topography where conventional methods are limited; an improved filter for the final interferometric phase the goal of which is to remove any remaining noise (for instance, noise caused by temporal decorrelation) while, simultaneously, phase details are appropriately preserved; and polarimetric optimization algorithms which also try to enhance the quality of the phase by exploring all the polarization diversity. Moreover, the exploitation of InSAR data for crop type mapping has also been evaluated in this thesis. Specifically, we have tested if the multitemporal interferometric coherence is a valuable feature which can be used as input to a machine learning algorithm to generate thematic maps of crop types. We have shown that InSAR data are sensitive to the temporal evolution of crops, and, hence, they constitute an alternative or a complement to conventional radiometric, SAR-based, classifications.
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Ilea, Ioana. "Robust classifcation methods on the space of covariance matrices. : application to texture and polarimetric synthetic aperture radar image classification." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0006/document.
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Au cours de ces dernières années, les matrices de covariance ont montré leur intérêt dans de nombreuses applications en traitement du signal et de l'image.Les travaux présentés dans cette thèse se concentrent sur l'utilisation de ces matrices comme descripteurs pour la classification. Dans ce contexte, des algorithmes robustes de classification sont proposés en développant les aspects suivants.Tout d'abord, des estimateurs robustes de la matrice de covariance sont utilisés afin de réduire l'impact des observations aberrantes. Puis, les distributions Riemannienne Gaussienne et de Laplace, ainsi que leur extension au cas des modèles de mélange, sont considérés pour la modélisation des matrices de covariance.Les algorithmes de type k-moyennes et d'espérance-maximisation sont étendus au cas Riemannien pour l'estimation de paramètres de ces lois : poids, centroïdes et paramètres de dispersion. De plus, un nouvel estimateur du centroïde est proposé en s'appuyant sur la théorie des M-estimateurs : l'estimateur de Huber. En outre,des descripteurs appelés vecteurs Riemannien de Fisher sont introduits afin de modéliser les images non-stationnaires. Enfin, un test d'hypothèse basé sur la distance géodésique est introduit pour réguler la probabilité de fausse alarme du classifieur.Toutes ces contributions sont validées en classification d'images de texture, de signaux du cerveau, et d'images polarimétriques radar simulées et réellesIn the recent years, covariance matrices have demonstrated their interestin a wide variety of applications in signal and image processing. The workpresented in this thesis focuses on the use of covariance matrices as signatures forrobust classification. In this context, a robust classification workflow is proposed,resulting in the following contributions.First, robust covariance matrix estimators are used to reduce the impact of outlierobservations, during the estimation process. Second, the Riemannian Gaussianand Laplace distributions as well as their mixture model are considered to representthe observed covariance matrices. The k-means and expectation maximization algorithmsare then extended to the Riemannian case to estimate their parameters, thatare the mixture's weight, the central covariance matrix and the dispersion. Next,a new centroid estimator, called the Huber's centroid, is introduced based on thetheory of M-estimators. Further on, a new local descriptor named the RiemannianFisher vector is introduced to model non-stationary images. Moreover, a statisticalhypothesis test is introduced based on the geodesic distance to regulate the classification false alarm rate. In the end, the proposed methods are evaluated in thecontext of texture image classification, brain decoding, simulated and real PolSARimage classification
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Cronin, Natasha Louise Rafaelle School of Biological Earth & Environmental Sciences UNSW. "The potential of airborne polarimetric synthetic aperture radar data for quantifying and mapping the biomass and structural diversity of woodlands in semi-arid Australia." Awarded by:University of New South Wales. School of Biological, Earth and Environmental Sciences, 2004. http://handle.unsw.edu.au/1959.4/30533.
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Levels of carbon dioxide in the atmosphere have been steadily increasing since the beginning of the Industrial Revolution in the 1800s. The earth's climate is sensitive to alterations in these levels of carbon dioxide and other greenhouse gases (GHG), with significant changes in climate predicted long term. The adoption of the Kyoto Protocol in 1997 heralded a new age in terms of greenhouse gas accounting and emissions responsibility, for all nations. In Australia, carbon emissions from the Land Use and Land Use Change and Forestry sector are responsible for a large proportion of the national total emissions. Radar remote sensing has demonstrated considerable potential in the estimation and mapping of vegetation biomass and subsequently carbon. The aim of this research is to investigate the potential of airborne polarimetric radar for quantifying and mapping the biomass and structural diversity of woodlands in semi-arid Australia. Initial investigation focussed on the physical structure of the woodland, which revealed that despite a diversity of woodland associations, the species diversity was relatively low. Both excurrent and decurrent growth forms were present, which subsequently resulted in varying allocation of biomass to the components (i.e., branches, trunks). In view of this, both empirical and modelling methodologies were explored. Empirical relationships were established between SAR backscatter and the total above ground biomass. Considerable scatter was present in these relationships, which was attributed to the large range of species and their associated structures. Comparison of actual and model simulations for C-, L- and P-band wavelengths, reveal that no significant difference existed for these wavelengths, except at CHH, and the cross-polarised data at L- and P-band. The study confirmed that microwaves at C-band interacted largely with the leaves and small branches, with scattering at VV polarization dominating. Compared to the lower frequencies, the return from the ground surface (as expected) was significant. The differences in scattering mechanisms (i.e., branch-ground versus trunk-ground) between excurrent and decurrent structures were due largely to the larger angular branches associated with Eucalyptus and Angophora species, which were absent from Callitris glaucophylla.
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De, Beyer Leigh Helen. "Integrated use of polarimetric Synthetic Aperture Radar (SAR) and optical image data for land cover mapping using an object-based approach." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97934.
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Thesis (MA)--Stellenbosch University, 2015.ENGLISH ABSTRACT: Image classification has long been used in earth observation and is driven by the need for accurate maps to develop conceptual and predictive models of Earth system processes. Synthetic aperture radar (SAR) imagery is used ever more frequently in land cover classification due to its complementary nature with optical data. There is therefore a growing need for reliable, accurate methods for using SAR and optical data together in land use and land cover classifications. However, combining data sets inevitably increases data dimensionality and these large, complex data sets are difficult to handle. It is therefore important to assess the benefits and limitations of using multi-temporal, dual-sensor data for applications such as land cover classification. This thesis undertakes this assessment through four main experiments based on combined RADARSAT-2 and SPOT-5 imagery of the southern part of Reunion Island. In Experiment 1, the use of feature selection for dimensionality reduction was considered. The rankings of important features for both single-sensor and dual-sensor data were assessed for four dates spanning a 6-month period, which coincided with both the wet and dry season. The mean textural features produced from the optical bands were consistently ranked highly across all dates. In the two later dates (29 May and 9 August 2014), the SAR features were more prevalent, showing that SAR and optical data have complementary natures. SAR data can be used to separate classes when optical imagery is insufficient. Experiment 2 compared the accuracy of six supervised and machine learning classification algorithms to determine which performed best with this complex data set. The Random Forest classification algorithm produced the highest accuracies and was therefore used in Experiments 3 and 4. Experiment 3 assessed the benefits of using combined SAR-optical imagery over single-sensor imagery for land cover classifications on four separate dates. The fused imagery produced consistently higher overall accuracies. The 29 May 2014 fused data produced the best accuracy of 69.8%. The fused classifications had more consistent results over the four dates than the single-sensor imagery, which suffered lower accuracies, especially for imagery acquired later in the season. In Experiment 4, the use of multi-temporal, dual-sensor data for classification was evaluated. Feature selection was used to reduce the data set from 638 potential training features to 50, which produced the best accuracy of 74.1% in comparison to 71.9% using all of the features. This result validated the use of multi-temporal data over single-date data for land cover classifications. It also validated the use of feature selection to successfully inform data reduction without compromising the accuracy of the final product. Multi-temporal and dual-sensor data shows potential for mapping land cover in a tropical, mountainous region that would otherwise be challenging to map using single-sensor data. However, accuracies Stellenbosch University https://scholar.sun.ac.za iv generally remained lower than would allow for transferability and replication of the current methodology. Classification algorithm optimisation, supervised segmentation and improved training data should be considered to improve these results.
AFRIKAANSE OPSOMMING: Beeld-klassifikasie word al ‘n geruime tyd in aardwaarneming gebruik en word gedryf deur die behoefte aan akkurate kaarte om konseptuele en voorspellende modelle van aard-stelsel prosesse te ontwikkel. Sintetiese apertuur radar (SAR) beelde word ook meer dikwels in landdekking klassifikasie gebruik as gevolg van die aanvullende waarde daarvan met optiese data. Daar is dus 'n groeiende behoefte aan betroubare, akkurate metodes vir die gesamentlike gebruik van SAR en optiese data in landdekking klassifikasies. Die kombinasie van datastelle bring egter ‘n onvermydelike verhoging in data dimensionaliteit mee, en hierdie groot, komplekse datastelle is moeilik om te hanteer. Dus is dit belangrik om die voordele en beperkings van die gebruik van multi-temporale, dubbel-sensor data vir toepassings soos landdekking-klassifikasie te evalueer. Die waarde van gekombineerde (versmelte) RADARSAT-2 en SPOT-5 beelde word in hierdie tesis deur middel van vier eksperimente geevalueer. In Eksperiment 1 is die gebruik van kenmerk seleksie vir dimensionaliteit-vermindering toegepas. Die ranglys van belangrike kenmerke vir beide enkel-sensor en 'n dubbel-sensor data is beoordeel vir vier datums wat oor 'n tydperk van 6 maande strek. Die gemiddelde tekstuur kenmerke uit die optiese lae is konsekwent hoog oor alle datums geplaas. In die twee later datums (29 Mei en 9 Augustus 2014) was die SAR kenmerke meer algemeen, wat dui op die aanvullende aard van SAR en optiese data. SAR data dus gebruik kan word om klasse te onderskei wanneer optiese beelde onvoldoende daarvoor is. Eksperiment 2 het die akkuraatheid van ses gerigte en masjien-leer klassifikasie algoritmes vergelyk om te bepaal watter die beste met hierdie komplekse datastel presteer. Die random gorest klassifikasie algoritme het die hoogste akkuraatheid bereik en is dus in Eksperimente 3 en 4 gebruik. Eksperiment 3 het die voordele van gekombineerde SAR-optiese beelde oor enkel-sensor beelde vir landdekking klassifikasies op vier afsonderlike datums beoordeel. Die versmelte beelde het konsekwent hoër algehele akkuraathede as enkel-sensor beelde gelewer. Die 29 Mei 2014 data het die hoogste akkuraatheid van 69,8% bereik. Die versmelte klassifikasies het ook meer konsekwente resultate oor die vier datums gelewer en die enkel-sensor beelde het tot laer akkuraathede gelei, veral vir die later datums. In Eksperiment 4 is die gebruik van multi-temporale, dubbel-sensor data vir klassifikasie ge-evalueer. Kenmerkseleksie is gebruik om die data stel van 638 potensiële kenmerke na 50 te verminder, wat die beste akkuraatheid van 74,1% gelewer het. Hierdie resultaat bevestig die belangrikheid van multi-temporale data vir grond dekking klassifikasies. Dit bekragtig ook die gebruik van kenmerkseleksie om data vermindering suksesvol te rig sonder om die akkuraatheid van die finale produk te belemmer. Stellenbosch University https://scholar.sun.ac.za vi Multi-temporale en dubbel-sensor data toon potensiaal vir die kartering van landdekking in 'n tropiese, bergagtige streek wat andersins uitdagend sou wees om te karteer met behulp van enkel-sensor data. Oor die algemeen het akkuraathede egter te laag gebly om vir oordraagbaarheid en herhaling van die huidige metode toe te laat. Klassifikasie algoritme optimalisering, gerigte segmentering en verbeterde opleiding data moet oorweeg word om hierdie resultate te verbeter.
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Ishitsuka, Kazuya. "Synthetic Aperture Radar Interferometry Time-series for Surface Displacement Monitoring: Data interpretation and improvement in accuracy." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199261.
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Trisasongko, Bambang Physical Environmental & Mathematical Sciences Australian Defence Force Academy UNSW. "Monitoring a mine-influenced environment in Indonesia through radar polarimetry." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/39747.
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Although remotely sensed data have been employed to assess various environmental problems, relatively few previous studies have focused on the impacts of mining. In Indonesia, mining activities have increasingly become one of major drivers of land cover change. The majority of remote sensing research projects on mining environments have exploited optical data which are frequently complicated by tmospheric disturbance, especially in tropical territories. Active remote sensors such as Synthetic Aperture Radar (SAR) are invaluable in this case. Monitoring by Independent SAR data has been limited due to single polarisation. Dual-polarised data have been employed considerably, although for some forestry applications the data were found insufficient to retrieve basic information. This Masters thesis is devoted to assess fully polarimetric SAR data for environmental monitoring of the tailings deposition zone of the PT Freeport Indonesia Grasberg mine in Papua, Indonesia. The main data were two granules of the AIRSAR datasets acquired during the PACRIM-II campaign. To support the interpretation and analysis, a scene of Landsat ETM February 2001) was used, juxtaposed with classified aerial photographs and a series of SPOT VEGETATION images. Both backscattering information and complex coherence matrices, as common representations of polarimetric data, were studied. Primary applications of this research were on degraded forest and environmental rehabilitation. Most parts of Indonesian forests have experienced abrupt changes as an impact of clear-cut deforestation. Gradual changes such as those due to fire or flooded tailings, however, are least studied. It was shown that the Cloude-Pottier polarimetric decomposition provided a convenient way to interpret various stages of forest disturbance. The result suggested that the Entropy parameter of the Cloude-Pottier decomposition could be used as a disturbance indicator. Using the fully polarimetric dataset combined with Support Vector Machine learning, the outcomes were generally acceptable. It was possible to improve classification accuracy by incorporating decomposition parameters, although it seemed insignificant. Land rehabilitation on tailings deposits has been a central concern of the government and the mining operator. Indigenous plant pioneers such as reeds (Phragmites) can naturally grow on dry tailings where soil structure is fairly well developed. To assist such efforts, a part of this research involved identification of dry tailings. On the first assessment, interpretation of surface scatterers was aided by polarimetric signatures. Apparently, longer wavelengths such as L- and P-band were overpenetrated; hence, growing reeds on dry tailings were less detectable. In this case, the use of C-band data was found fairly robust. Employing Mahalanobis statistics, the combination of HH and VV performed well on classification, having similar accuracy with quad polarimetric data. Extension on previous results was made through the Freeman-Durden decomposition. Interpretation using a three-component image of odd, even bounce and volume scattering showed that dry and wet tailings could be well distinguished. The application was benefited from unique responses of dielectric materials in the tailings deposit on SAR signals; hence it is possible to discriminate tailings with different moisture levels. However, further assessment of tailings moisture was not possible due to security reasons and access limitations at the study site. Fully polarimetric data were also employed to support rehabilitation of stressed mangrove forest on the southern coast. In this case, the Cloude-Pottier decomposition was employed along with textural parameters. Inclusion of textural properties was found invaluable for the classification using various statistical trees, and more important than decomposition parameters. It was concluded that incorporating polarimetric decompositions and textural parameters into coherence matrix leads to profound accuracy.
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Shirvany, Réza. "Estimation of the Degree of Polarization in Polarimetric SAR Imagery : Principles and Applications." Thesis, Toulouse, INPT, 2012. http://www.theses.fr/2012INPT0082/document.
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Les radars à synthèse d’ouverture (RSO) polarimétriques sont devenus incontournables dans le domaine de la télédétection, grâce à leur zone de couverture étendue, ainsi que leur capacité à acquérir des données dans n’importe quelles conditions atmosphériques de jour comme de nuit. Au cours des trois dernières décennies, plusieurs RSO polarimétriques ont été utilisés portant une variété de modes d’imagerie, tels que la polarisation unique, la polarisation double et également des modes dits pleinement polarimétriques. Grâce aux recherches récentes, d’autres modes alternatifs, tels que la polarisation hybride et compacte, ont été proposés pour les futures missions RSOs. Toutefois, un débat anime la communauté de la télédétection quant à l’utilité des modes alternatifs et quant au compromis entre la polarimétrie double et la polarimétrie totale. Cette thèse contribue à ce débat en analysant et comparant ces différents modes d’imagerie RSO dans une variété d’applications, avec un accent particulier sur la surveillance maritime (la détection des navires et de marées noires). Pour nos comparaisons, nous considérons un paramètre fondamental, appelé le degré de polarisation (DoP). Ce paramètre scalaire a été reconnu comme l’un des paramètres les plus pertinents pour caractériser les ondes électromagnétiques partiellement polarisées. A l’aide d’une analyse statistique détaillée sur les images polarimétriques RSO, nous proposons des estimateurs efficaces du DoP pour les systèmes d’imagerie cohérente et incohérente. Ainsi, nous étendons la notion de DoP aux différents modes d’imagerie polarimétrique hybride et compacte. Cette étude comparative réalisée dans différents contextes d’application dégage des propriétés permettant de guider le choix parmi les différents modes polarimétriques. Les expériences sont effectuées sur les données polarimétriques provenant du satellite Canadian RADARSAT-2 et le RSO aéroporté Américain AirSAR, couvrant divers types de terrains tels que l’urbain, la végétation et l’océan. Par ailleurs nous réalisons une étude détaillée sur les potentiels du DoP pour la détection et la reconnaissance des marées noires basée sur les acquisitions récentes d’UAVSAR, couvrant la catastrophe de Deepwater Horizon dans le golfe du MexiquePolarimetric Synthetic Aperture Radar (SAR) systems have become highly fruitful thanks to their wide area coverage and day and night all-weather capabilities. Several polarimetric SARs have been flown over the last few decades with a variety of polarimetric SAR imaging modes; traditional ones are linear singleand dual-pol modes. More sophisticated ones are full-pol modes. Other alternative modes, such as hybrid and compact dual-pol, have also been recently proposed for future SAR missions. The discussion is vivid across the remote sensing society about both the utility of such alternative modes, and also the trade-off between dual and full polarimetry. This thesis contributes to that discussion by analyzing and comparing different polarimetric SAR modes in a variety of geoscience applications, with a particular focus on maritime monitoring and surveillance. For our comparisons, we make use of a fundamental, physically related discriminator called the Degree of Polarization (DoP). This scalar parameter has been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. Based on a detailed statistical analysis of polarimetric SAR images, we propose efficient estimators of the DoP for both coherent and in-coherent SAR systems. We extend the DoP concept to different hybrid and compact SAR modes and compare the achieved performance with different full-pol methods. We perform a detailed study of vessel detection and oil-spill recognition, based on linear and hybrid/compact dual-pol DoP, using recent data from the Deepwater Horizon oil-spill, acquired by the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). Extensive experiments are also performed over various terrain types, such as urban, vegetation, and ocean, using the data acquired by the Canadian RADARSAT-2 and the NASA/JPL Airborne SAR (AirSAR) system
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Ullmann, Tobias [Verfasser], Roland [Gutachter] Baumhauer, Stefan [Gutachter] Dech, and Hans-Wolfgang [Gutachter] Hubberten. "Characterization of Arctic Environment by Means of Polarimetric Synthetic Aperture Radar (PolSAR) Data and Digital Elevation Models (DEM) / Tobias Ullmann. Gutachter: Roland Baumhauer ; Stefan Dech ; Hans-Wolfgang Hubberten." Würzburg : Universität Würzburg, 2015. http://d-nb.info/111178387X/34.
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Barrachina, Jose Agustin. "Complex-valued neural networks for radar applications." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG094.
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Le traitement des signaux radars et des images SAR nécessite généralement des représentations et des opérations à valeurs complexes, telles que les transformées de Fourier et d'ondelettes, les filtres de Wiener et les filtres adaptés, etc. Cependant, la grande majorité des architectures d'apprentissage profond sont actuellement basées sur des opérations à valeurs réelles, ce qui limite leur capacité d'apprentissage à partir de données complexes. Malgré l'émergence des réseaux de neurones à valeurs complexes (CVNN), leur application au radar et à l'imagerie SAR manque encore d'études sur leur pertinence et leur efficacité. Et la comparaison avec un réseau de neurones à valeurs réelles (RVNN) équivalent est généralement biaisée.Dans cette thèse, nous proposons d'étudier les mérites des CVNNs pour classifier des données complexes. Nous montrons que les CVNNs atteignent de meilleures performances que leur equivalent réel pour classifier des vecteurs à données gaussiennes non circulaires. Nous définissons également un critère d'équivalence entre les CVNNs et les RVNNs, entièrement connectés ou convolutifs, en termes du nombre de paramètres entraînables, tout en leur conservant une architecture similaire. Nous comparons ainsi statistiquement les performances de perceptrons multicouches (MLPs), de réseaux convolutifs (CNNs) et entièrement convolutifs (FCNNs) utilisés pour la segmentation d'images SAR polarimétriques. Le partitionnement des images SAR et l'équilibrage des classes sont étudiés afin d'éviter des biais d'apprentissage. En parallèle, nous avons également proposé une librairie open-source pour faciliter l'implémentation des CVNNs et la comparaison avec des réseaux équivalents réelsRadar signal and SAR image processing generally require complex-valued representations and operations, e.g., Fourier, wavelet transforms, Wiener, matched filters, etc. However, the vast majority of architectures for deep learning are currently based on real-valued operations, which restrict their ability to learn from complex-valued features. Despite the emergence of Complex-Valued Neural Networks (CVNNs), their application on radar and SAR still lacks study on their relevance and efficiency. And the comparison against an equivalent Real-Valued Neural Network (RVNN) is usually biased.In this thesis, we propose to investigate the merits of CVNNs for classifying complex-valued data. We show that CVNNs achieve better performance than their real-valued counterpart for classifying non-circular Gaussian data. We also define a criterion of equivalence between feed-forward fully connected and convolutional CVNNs and RVNNs in terms of trainable parameters while keeping a similar architecture. We statistically compare the performance of equivalent Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Fully Convolutional Neural Networks (FCNNs) for polarimetric SAR image segmentation. SAR image splitting and balancing classes are also studied to avoid learning biases. In parallel, we also proposed an open-source toolbox to facilitate the implementation of CVNNs and the comparison with real-equivalent networks
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El, Hajj Chehade Bassam. "Traitements tomographiques pour la caractérisation de forêts tropicales à l'aide des données SAR polarimétriques." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S081.
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Dans le cycle de carbone à l'échelle de la planète, la contribution des forêts tropicales, en tant que stock de carbone, est déterminante. Les études actuelles montrent que la connaissance précise de la biomasse forestière globale est nécessaire pour les modèles de prévision. C'est dans ce contexte que le projet BIOMASS est choisi par l'Agence spatiale européenne (ESA) comme une phase A du programme «Earth Core Mission». L'objectif de cette mission innovatrice est l'utilisation d'un système d'imagerie polarimétrique fonctionnant en bande P (435 MHz) pour la mesure de la biomasse forestière. La définition actuelle de la mission prévoit un mode tomographique rassurant une imagerie tri-dimentionnelle (3-D) de la forêt. Dans le cadre du projet BIOMASS, cette thèse de doctorat vise à développer une nouvelle stratégie pour la télédétection de la biomasse dans les forêts tropicales en utilisant des données multi-baseline acquises par le radar à ouverture synthétique (SAR) en bande P. Une approche originale consite à combiner la tomographie et le modèle RvoG (Random-Volume-over-Ground) établi et vérifié avec la technique PolInSAR (polarimetric SAR Interferometry). L'environnement forestier peut être décrit avec précision par un modèle polarimétrique multicouche (sol et succession de couches végétales). Une généralisation multi-baseline du modèle RVoG implique un certain nombre de paramètres qui peuvent être estimés à partir des données SAR en utilisant des méthodes spectrales haute résolution. Ainsi, une cartographie de la forêt et du sol peut être réalisée à l'aide de données tomographiques. De plus, la capacité des techniques tomographiques permet d'estimer la distribution verticale de la puissance rétrodiffusée. Ainsi, une information précise sur la biomasse peut être extraite de la puissance mesurée dans un domaine adapté à la couche de végétation. Cependant, cette puissance mesurée peut être fortement affectée par l'écho du sol dû à la contribution de double rebond. Et par suite, le principal défi peut être résumé par l'élaboration d'un nouvel estimateur de la biomasse forestière lié à une puissance rétrodiffusée mesurée avec une polarisation et un domaine vertical, tous les deux sont adaptés à la couche de végétation. Les algorithmes développés pour la cartographie de la forêt, l'estimation et la simulation de la biomasse sont appliqués et validés sur des données SAR aéroportées réalisées lors de la campagne TROPISAR en GuyaneForested areas cover one third of earth's land surface and their contribution in the storage of carbon is decisive. Current studies show that the accurate knowledge of global forest biomass is necessary for the prediction of climate changes on the planet. In this context, the BIOMASS project is selected by the European Space Agency (ESA) as Phase A of the 'Earth Core Mission' program. This highly innovative mission consists of the use of a polarimetric imaging radar operating at P band (435 MHz) for the measurement of forest biomass. The current definition of the mission provides a three-dimensional imaging (3-D) of the forest with both tomographic and multi-pass interferometric modes. In the framework of this project, this PHD thesis aims to develop a novel strategy for the remote sensing of the biomass within the dense tropical forests by processing on multi-baseline P-band Synthetic Aperture Radar (SAR) data. An original approach combines the possibilities of 3-D exploration tomography and the Random-Volume- over-Ground (RVoG) model established and verified with PolInSAR technique (Polarimetric Interferometry SAR). The forested environment can be accurately described by a polarimetric multi-layer model (soil and a succession of vegetationlayers). A multi-baseline generalization of the RVoG model involves a certain number of parameters which must be estimated from radar observation data by using High- Resolution spectral estimation tomographic methods. Thereby, a cartography of the forest and its underlying ground can be made using tomographic data. Furthermore, the capacity of the tomographic techniques on 3-D imaging allows an estimation of the vertical distribution of the backscattered power. Thus, an accurate biomass information may be extracted from the power measured at a domain adapted to the canopy layer. However, this measured backscattered may be strongly affected by the ground echo due to the double bounce contribution. The main challenge of this thesis is to establish a novel biomass estimator related to a backscattered powermeasured with a polarimetric channel and at a vertical domain, both adapted to the canopy layer. The proposed algorithms of forest cartography and biomass estimation are applied and validated on Airborne P-band SAR data realized on the TROPISAR campaign in French Guyana
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López, Martinez Carlos. "Multidimensional speckle noise. Modelling and filtering related to sar data." Doctoral thesis, Universitat Politècnica de Catalunya, 2003. http://hdl.handle.net/10803/6921.
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Los Radares de Apertura Sintética, o sistemas SAR, representan el mejorejemplo de sistemas activos de teledetección por microondas. Debido a su naturaleza coherente, un sistema SAR es capaz de adquirir información dedispersión electromagnética con una alta resolución espacial, pero por otro lado, esta naturaleza coherente provoca también la aparición de speckle.A pesar de que el speckle es una medida electromagnética, sólo puede ser analizada como una componente de ruido debido a la complejidad asociadacon el proceso de dispersión electromagnética.Para eliminar los efectos del ruido speckle adecuadamente, es necesario un modelo de ruido, capaz de identificar las fuentes de ruido y como éstasdegradan la información útil. Mientras que este modelo existe para sistemasSAR unidimensionales, conocido como modelo de ruido speckle multiplicativo,éste no existe en el caso de sistemas SAR multidimensionales.El trabajo presentado en esta tesis presenta la definición y completa validación de nuevos modelos de ruido speckle para sistemas SAR multidimensionales,junto con su aplicación para la reducción de ruido speckle y la extracción de información.En esta tesis, los datos SAR multidimensionales, se consideran bajo una formulación basada en la matriz de covarianza, ya que permite el análisisde datos sobre la base del producto complejo Hermítico de pares de imágenesSAR. Debido a que el mantenimiento de la resolución especial es un aspectoimportante del procesado de imágenes SAR, la reducción de ruido speckleestá basada, en este trabajo, en la teoría de análisis wavelet.
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Wasik, Valentine. "Analyse de la précision d’estimation de deux systèmes d’imagerie polarimétrique." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4348.
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L’imagerie polarimétrique permet d’estimer certaines caractéristiques d’un milieu qui peuvent ne pas être révélées par imagerie d’intensité standard. Cependant, les mesures effectuées peuvent être fortement perturbées par des fluctuations inhérentes aux processus physiques d’acquisition. Ces fluctuations sont difficiles à atténuer, notamment à cause de la fragilité des milieux observés ou de l’inhomogénéité des images acquises. Il est alors utile de caractériser la précision des estimations qu’il est possible d’obtenir. Dans cette thèse, cette question est abordée au travers de deux applications d’imagerie polarimétrique : la microscopie non-linéaire de second harmonique résolue en polarisation (PSHG) pour l’analyse de l’organisation structurale d’objets biomoléculaires, et l’imagerie radar polarimétrique interférométrique à synthèse d’ouverture (PolInSAR) pour l’estimation des paramètres du couvert forestier. Pour la première application, la précision d’estimation en présence de bruit de Poisson est caractérisée pour l’ensemble des assemblages moléculaires présentant une symétrie cylindrique, ce qui permet notamment d'aboutir à une procédure de détection des mesures qui ne permettent pas d’atteindre une précision d’estimation requise. Pour l’imagerie PolInSAR, on analyse une modalité d'acquisition intéressante pour les futures missions satellitaires. En particulier, on étudie dans ce contexte la précision d'estimation de la hauteur de végétation en présence de bruit de speckle en s'appuyant sur l'analyse du contraste polarimétrique. Une interprétation simple des comportements de cette modalité d'acquisition est obtenue dans la sphère de PoincaréPolarimetric imaging allows one to estimate some characteristics of a medium which might not be revealed by standard intensity imaging. However, the measurements can be strongly perturbed by fluctuations that are inherent in the physical acquisition processes. These fluctuations are difficult to attenuate, for instance because of the fragility of the observed media or because of the inhomogeneity of the obtained images. It is then useful to characterize the estimation precision that can be reached. In this thesis, this question is addressed through two polarimetric imaging applications: polarized-resolved second-harmonic generation non-linear microscopy (PSHG) for the analysis of the structural organization of biomolecular objects, and polarimetric interferometric synthetic aperture radar imaging (PolInSAR) for the estimation of vegetation parameters. For the first application, the estimation precision in the presence of Poisson noise is characterized for any molecular assembly that presents a cylindrical symmetry. This study results in particular in a procedure to detect the measurements that do not lead to a required precision. For PolInSAR imaging, we analyze an acquisition system that is interesting for future spatial missions. In particular, the estimation precision of the vegetation height is studied in this context in the presence of speckle noise by relying on the analysis of the polarimetric contrast. A simple interpretation of the behavior of this acquisition system is obtained in the Poincaré sphere
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Nasonova, Sasha. "Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt." Thesis, Remote Sensing, 2017. https://dspace.library.uvic.ca//handle/1828/9313.
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Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to discriminate between major ice types during winter and advanced melt, with a focus on advanced melt. RCM parameters with highest discrimination ability in conjunction with optimal GLCM texture features were used as input parameters for Support Vector Machine (SVM) supervised classifications. The results indicate that steep incidence angle RCM parameters show promise for distinguishing between FYI and MYI during advanced melt with an overall classification accuracy of 77.06%. The addition of GLCM texture parameters improved accuracy to 85.91%. This thesis provides valuable contributions to the growing body of literature on fp parameterization and SAR ice type discrimination during advanced melt.Graduate
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Yates, Gillian. "Bistatic synthetic aperture radar." Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1446870/.
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Synthetic aperture radar (SAR) allows all-weather, day and night, surface surveillance and has the ability to detect, classify and geolocate objects at long stand-off ranges. Bistatic SAR, where the transmitter and the receiver are on separate platforms, is seen as a potential means of countering the vulnerability of conventional monostatic SAR to electronic countermeasures, particularly directional jamming, and avoiding physical attack of the imaging platform. As the receiving platform can be totally passive, it does not advertise its position by RF emissions. The transmitter is not susceptible to jamming and can, for example, operate at long stand-off ranges to reduce its vulnerability to physical attack. This thesis examines some of the complications involved in producing high-resolution bistatic SAR imagery. The effect of bistatic operation on resolution is examined from a theoretical viewpoint and analytical expressions for resolution are developed. These expressions are verified by simulation work using a simple 'point by point' processor. This work is extended to look at using modern practical processing engines for bistatic geometries. Adaptations of the polar format algorithm and range migration algorithm are considered. The principal achievement of this work is a fully airborne demonstration of bistatic SAR. The route taken in reaching this is given, along with some results. The bistatic SAR imagery is analysed and compared to the monostatic imagery collected at the same time. Demonstrating high-resolution bistatic SAR imagery using two airborne platforms represents what I believe to be a European first and is likely to be the first time that this has been achieved outside the US (the UK has very little insight into US work on this topic). Bistatic target characteristics are examined through the use of simulations. This also compares bistatic imagery with monostatic and gives further insight into the utility of bistatic SAR.
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Mak, Karen. "Arrayed synthetic aperture radar." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/32382.
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In this thesis, the use of array processing techniques applied to Single Input Multiple Output (SIMO) SAR systems with enhanced capabilities is investigated. In Single Input Single Output (SISO) SAR systems there is a high resolution, wide swath contradiction, whereby it is not possible to increase both cross-range resolution and the imaged swath width simultaneously. To overcome this, a novel beamformer for SAR systems in the cross-range direction is proposed. In particular, this beamformer is a superresolution beamformer capable of forming wide nulls using subspace based approaches. SIMO SAR systems also give rise to additional sets of received data, which includes geometrical information about the SAR and target environment, and can be used for enhanced target parameter estimation. In particular, this thesis looks at round trip delay, joint azimuth and elevation angle, and relative target power estimation. For round trip delay estimation, the use of the traditional matched filter with subspace partitioning is proposed. Then by using a joint 2D Multiple Signal Classification (MUSIC) algorithm, joint Direction of Arrival (DOA) estimation can be achieved. Both the use of range lines of raw SAR data and the use of a Region of Interest (ROI) of a SAR image are investigated. However in terms of imaging, MUSIC is not well-suited for SAR, due to its target response not corresponding to the target's true power return. Therefore a joint DOA and target power estimation algorithm is proposed to overcome this limitation. These algorithms provide the framework for the development of three processing techniques. These allow sidelobe suppression in the slant range direction, along with the reconstruction of undersampled data and region enhancement using MUSIC with power preservation.
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Chua, Cheng Lock Charles. "Doppler-only synthetic aperture radar." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FChua.pdf.
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Thesis (M.S. in Combat Systems Sciences and Technology)--Naval Postgraduate School, December 2006.Thesis Advisor(s): Brett Borden, Donald Walters. "December 2006." Includes bibliographical references (p. 69-70). Also available in print.
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Duersch, Michael Israel. "Backprojection for Synthetic Aperture Radar." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/4060.
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Synthetic aperture radar (SAR) is a type of radar capable of high-resolution coherent imaging. In order to produce coherent imagery from raw SAR data, an image formation algorithm is employed. The various image formation algorithms have strengths and weaknesses. As this work shows, time-domain backprojection is one algorithm whose strengths are particularly well-suited to use at low-altitudes. This work presents novel research in three areas regarding time-domain backprojection. The first key contribution of this work is a detailed analysis of SAR time-domain backprojection. The work derives a general form of backprojection from first principles. It characterizes the sensitivities of backprojection to the various inputs as well as error sources and performance characteristics. This work then shows what situations are particularly well-suited to use of the backprojection algorithm, namely regimes with turbulent motion and wide variation in incidence angle across the range swath (e.g., low-altitude, airborne SAR).The second contribution of this work is an analysis of geometric signal correlation for multi-static, sometimes termed multiple-input and multiple-output (MIMO), imaging. Multi-static imaging involves forming multiple images using different combinations of transmitters and receivers. Geometric correlation is a measure of how alike observations of a target are from different aspect angles. This work provides a novel model for geometric correlation which may be used to determine the degree to which multi-static images are correlated. This in turn determines their applicable use: operating in the highly correlated regime is desirable for coherent processing whereas operating in a lower-correlation regime is desirable for obtaining independent looks. The final contribution of this work is a novel algorithm for interferometry based on backprojected data. Because of the way backprojected images are formed, they are less suited to traditional interferometric methods. This work derives backprojection interferometry and compares it to the traditional method of interferometry. The sensitivity and performance of backprojection interferometry are shown, as well as where backprojection interferometry offers superior results. This work finds that backprojection interferometry performs better with longer interferometric baseline lengths or systems with large measurement error in the baseline length or angle (e.g., low-altitude, airborne SAR).
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She, Zhishun. "Array processing methods for calibrating Inverse Synthetic Aperture Radar and Multiple Pass Synthetic Aperture Radar." Title page, contents and abstract only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phs5389.pdf.
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Table of corrections inserted opposite table of contents. Bibliography: p.191-212. Investigates calibration for errors of a synthetic aperture in Inverse Synthetic Aperture Radar and Multiple Pass Synthetic Aperture Radar. Both are reviewed as the problems of array processing and are solved from the point of array calibration.
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Hagedorn, Michael. "Classification of synthetic aperture radar images." Thesis, University of Canterbury. Electrical and Computer Engineering, 2004. http://hdl.handle.net/10092/5966.
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In this thesis the maximum a posteriori (MAP) approach to synthetic aperture radar (SAR) analysis is reviewed. The MAP model consists of two probability density functions (PDFs): the likelihood function and the prior model. Contributions related to both models are made.
As the first contribution a new likelihood function describing the multilook three-polarisation intensity SAR speckle process, which is equivalent to the averaged squared amplitude samples from a three-dimensional complex zero-mean circular Gaussian density, has been derived. This PDF is a correlated three-dimensional chi-square density in the form of an infinite series of modified Bessel functions with seven independent parameters. Details concerning the PDF such as the estimation of the PDF parameters from sample data and the moments of the PDF are described. The new likelihood function is tested against simulated and measured SAR data.
The second contribution is a novel parameter estimation method for discrete Gibbs random field (GRF) prior models. Given a quantity of sample data, the parameters of the GRF model, which comprise the values of the potential functions of individual cliques, are estimated. The method uses an error function describing the difference between the local model PDF and the equivalent estimated from sample data. The concept of "equivalencies" is introduced to simplify the process. The new parameter estimation method is validated and compared to Besag's parameter estimation method (coding method) using GRF realisations and other sample data.
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Bhattacharya, Sujit. "Coding of synthetic aperture radar data." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/11959.
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Synthetic Aperture Radar (SAR) is a dedicated high-resolution sensor with imaging capability in all weather and day-night conditions and has been employed in several earth and interplanetary observation applications. A significant characteristic of this system is the generation of a large amount of data that involves major problems related to on-board data storage and downlink transmission. The near future SAR satellite missions planned would be pushing downlink data bandwidths to prohibitive levels, which dictate efficient on-board compression of raw data. Due to the limitation of the on-board resources in the satellite, it is desirable to have computationally efficient encoder. In this thesis we address the compression of complex-valued SAR raw data in the Compressed Sensing (CS) framework, in which the encoder is simple whereas the decoder is computational expensive. CS is an emerging technique for signal measurement and reconstruction that takes advantage of the fact that many signals are sparse under some basic or frame. The measurement of the signal in the CS framework is obtained by taking a small number of projections of the signal onto an incoherent basis. For the SAR raw data compression here we have considered a simple encoder, with a 2D-FFT followed by a random sampler. The reconstruction of the sparse coefficients of the signal from these projections is then based on the sparsity induced optimization techniques like Orthogonal Matching Pursuit (OMP) and iterative reconstruction methods. We demonstrate empirically that the CS framework for compression of complex-valued SAR raw data is effective for the cases when the SAR image is sparse in the spatial domain. We also address the limitations of this framework while dealing with actual satellite images due to lack of good sparsifying transforms for the complex-valued data. In this thesis, we present a new algorithm based on regularized iterative algorithm for finding sparse solution for the complex-valued data in which the regularization parameter is adaptively computed in each iteration. The effectiveness of the new algorithm is compared with existing methods like Basis Pursuit, OMP, etc with both real and complex data set.
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Kelly, Shaun Innes. "Iterative synthetic aperture radar imaging algorithms." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9368.
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Synthetic aperture radar is an important tool in a wide range of civilian and military imaging applications. This is primarily due to its ability to image in all weather conditions, during both the day and the night, unlike optical imaging systems. A synthetic aperture radar system contains a step which is not present in an optical imaging system, this is image formation. This is required because the acquired data from the radar sensor does not directly correspond to the image. Instead, to form an image, the system must solve an inverse problem. In conventional scenarios, this inverse problem is relatively straight forward and a matched lter based algorithm produces an image of suitable image quality. However, there are a number of interesting scenarios where this is not the case. Scenarios where standard image formation algorithms are unsuitable include systems with data undersampling, errors in the system observation model and data that is corrupted by radio frequency interference. Image formation in these scenarios will form the topics of this thesis and a number of iterative algorithms are proposed to achieve image formation. The motivation for these proposed algorithms is primarily from the eld of compressed sensing, which considers the recovery of signals with a low-dimensional structure. The rst contribution of this thesis is the development of fast algorithms for the system observation model and its adjoint. These algorithms are required by large-scale gradient based iterative algorithms for image formation. The proposed algorithms are based on existing fast back-projection algorithms, however, a new decimation strategy is proposed which is more suitable for some applications. The second contribution is the development of a framework for iterative near- eld image formation, which uses the proposed fast algorithms. It is shown that the framework can be used, in some scenarios, to improve the visual quality of images formed from fully sampled data and undersampled data, when compared to images formed using matched lter based algorithms. The third contribution concerns errors in the system observation model. Algorithms that correct these errors are commonly referred to as autofocus algorithms. It is shown that conventional autofocus algorithms, which work as a post-processor on the formed image, are unsuitable for undersampled data. Instead an autofocus algorithm is proposed which corrects errors within the iterative image formation procedure. The proposed algorithm is provably stable and convergent with a faster convergence rate than previous approaches. The nal contribution is an algorithm for ultra-wideband synthetic aperture radar image formation. Due to the large spectrum over which the ultra-wideband signal is transmitted, there is likely to be many other users operating within the same spectrum. These users can produce signi cant radio frequency interference which will corrupt the received data. The proposed algorithm uses knowledge of the RFI spectrum to minimise the e ect of the RFI on the formed image.
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Golda, Peter John. "Software simulation of synthetic aperture radar." Master's thesis, University of Cape Town, 1997. http://hdl.handle.net/11427/26092.
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The purpose of this report is to set out the results of the development of SAR simulation software. The aim of the thesis was to develop such software so that it provides the necessary functionality but is still flexible and simple to use. It addition it must be developed such that it may be compiled and run on as many platforms as possible and future functionality may be added with ease. All this in order to enable other RRSG members to obtain known simulated SAR data for the purpose of testing SAR processing algorithms.
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Matthew, Pianto Donald. "Modeling synthetic aperture radar image data." Universidade Federal de Pernambuco, 2008. https://repositorio.ufpe.br/handle/123456789/7128.
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Nessa tese estudamos a estimação por máxima verossimilhança (MV) do parâmetro de aspereza da distribuição G 0 A de imagens com speckle (Frery et al., 1997). Descobrimos que, satisfeita uma certa condição dos momentos amostrais, a função de verossimilhança é monótona e as estimativas MV são infinitas, implicando uma região plana. Implementamos quatro estimadores de correção de viés em uma tentativa de obter estimativas MV finitas. Três dos estimadores são obtidos da literatura sobre verossimilhança monótona (Firth, 1993; Jeffreys, 1946) e um, baseado em reamostragem, é proposto pelo autor. Fazemos experimentos numéricos de Monte Carlo para comparar os quatro estimadores e encontramos que não existe um favorito claro, a menos quando um parâmetro (dado a priori da estimação) toma um valor específico. Também aplicamos os estimadores a dados reais de radar de abertura sintética. O resultado desta análise mostra que os estimadores precisam ser comparados com base em suas habilidades de classificar regiões corretamente como ásperas, planas, ou intermediárias e não pelos seus vieses e erros quadráticos médios
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Green, Ryan K. "Scaled Synthetic Aperture Radar System Development." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1498.
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Synthetic Aperture Radar (SAR) systems generate two dimensional images of a target area using RF energy as opposed to light waves used by cameras. When cloud cover or other optical obstructions prevent camera imaging over a target area, SAR can be substituted to generate high resolution images. Linear frequency modulated signals are transmitted and received while a moving imaging platform traverses a target area to develop high resolution images through modern digital signal processing (DSP) techniques. The motivation for this joint thesis project is to design and construct a scaled SAR system to support Cal Poly radar projects. Objectives include low-cost, high resolution SAR architecture development for capturing images in desired target areas. To that end, a scaled SAR system was successfully designed, built, and tested. The current SAR system, however, does not perform azimuthal compression and range cell migration correction (image blur reduction). These functionalities can be pursued by future students joining the ongoing radar project. The SAR system includes RF modulating, demodulating, and amplifying circuitry, broadband antenna design, movement platform, LabView system control, and MATLAB signal processing. Each system block is individually described and analyzed followed by final measured data. To confirm system operation, images developed from data collected in a single target environment are presented and compared to the actual configuration.
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Legg, Jonathan Andrew. "Synthetic aperture radar using non-uniform sampling." Title page, contents and abstract only, 1997. http://web4.library.adelaide.edu.au/theses/09PH/09phl513.pdf.
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Bruno, Davide. "Geosynchronous synthetic aperture radar : design and applications." Thesis, Cranfield University, 2009. http://dspace.lib.cranfield.ac.uk/handle/1826/5618.
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Synthetic Aperture Radar (SAR) imaging from geosynchronous orbit has significant
potential advantages over conventional low-Earth orbit (LEO) radars, but also
challenges to overcome.
This thesis investigates both active and passive geosynchronous SAR configurations,
presenting their different features and advantages.
Following a system design trade-off that involved phase uncertainties, link budget,
frequency and integration time, an L band bi-static configuration with 8-hour integration
time that reuses the signal from a non-cooperative transmitter has been presented as a
suitable solution.
Cranfield Space Research Centre looked into this configuration and proposed the
GeoSAR concept, an L band bi-static SAR based on the concept by Prati et al. (1998).
It flies along a circular ground track orbit, reuses the signal coming from a noncooperative
transmitter in GEO and achieves a spatial resolution of about 100 m.
The present research contributes to the GeoSAR concept exploring the implications
due to the 8-hour integration time and providing insights about its performance and its
possible fields of application.
Targets such as canopies change their backscattered phase on timescales of seconds
due to their motion. On longer time scales, changes in dielectric properties of targets,
Earth tides and perturbations in the structure of the atmosphere contribute to generate
phase fluctuations in the collected signals. These phenomena bring temporal
decorrelation and cause a reduction in SAR coherent integration gain. They have to be
compensated for if useful images are to be provided.
A SAR azimuth simulator has been developed to study the influence of temporal
decorrelation on GeoSAR point spread function. The analysis shows that ionospheric
delay is the major source of decorrelation; other effects, such as tropospheric delay
and Earth tides, have to be dealt with but appear to be easier to handle.
Two different options for GeoSAR interferometry have been discussed. The system is
well suited to differential interferometry, due to the short perpendicular baseline
induced by the geometry. A GeoSAR has advantages over a Low Earth Orbit (LEO)
SAR system to monitor processes with significant variability over daily or shorter
timescales (e.g. soil moisture variation). This potential justifies further study of the
concept.
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Wanwiwake, Tippawan. "A microsatellite based synthetic aperture radar (SAR)." Thesis, University of Surrey, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548360.
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Garcia, Frank W. Jr. "Sea ice classification using synthetic aperture radar." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/27745.
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Approved for public release; distribution is unlimited.This study employs Synthetic Aperture Radar (SAR) imagery from the Marginal Ice Zone Experiment (MIZEX) 1987 to identify an optimal set of statistical descriptors that accurately classify three types of ice (first-year, multiyear, odden) and open water. Two groups of statistics, univariate and texture, are compared and contrasted with respect to their skill in classifying the ice types and open water. Individual statistical descriptors are subjected to principal component analysis and discriminant analysis. Principal component analysis was of little use in understanding features of each ice and open water group. Discriminant analysis was valuable in identifying which statistics held the most power. When combined, univariate and texture statistics classified the groups with 89.5% accuracy, univariate alone with 86.8% accuracy and texture alone with 75.4% accuracy. Range and inertia were the strongest univariate and texture discriminators with 74.6% and 50.8% accuracy, respectively. Despite the use of a non-calibrated SAR, univariate statistics were able to classify the images with greater accuracy than texture statistics.
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Lee, Woo Kyung. "Waveform design for advanced synthetic aperture radar." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342178.
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Wong, Wallace D. (Wallace Dazheng). "Synthetic Aperture Radar Interferometry with 3 satellites." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34120.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 125-128).
Our study investigates interferometric SAR (InSAR) post-processing height retrieval techniques. We explore the possible improvements by adding a third satellite to the two already in orbit, and examine some potential uses of this setup. As such, we investigate three methods for height retrieval and compare their results with the original 2-satellite method. The first approach is data averaging; a simple method that extends from the results obtained using the 2-satellite method. The 3 sets of data obtained per sampling look are grouped into pairs, and the 2 statistical best pairs are selected to be averaged, producing a better estimate of the digital elevation map (DEM) height. The second approach is the unambiguous range magnification (URM) method, which seeks to ease the reliance on phase unwrapping steps often necessary in retrieving height. It does so by expanding the wrapped phase range without performing any phase unwrapping, through the use of different wrapping speeds of the 3 sets of satellite pairings. The third method is the maximum likelihood estimation technique, an asymptotically efficient method which employs the same phase expansion property as the URM to predict the closest phase estimate which best fits most (if not all) of the data sets provided.
(cont.) Results show that for a handful of flyover looks, the data averaging method provides for an efficient and non-computationally intensive method for improving retrieved height results. This method can also help eliminate the need of GCPs in height retrieval, though such performance is limited by the presence of noise. The maximum likelihood method is shown to be asymptotically favorable over the data averaging method, if given a large number of flyover looks. The URM method performs worst, because it depends on the shortest baseline, which is most sensitive to noise, for unwrapping. Results are entirely simulation-based, using the engineering tool Matlab Version 6.1. Single- and multiple- trial simulations are compared for 1-dimensional interferograms only. In most cases, the root-mean-square error will be used as the metric for comparison.
by Wallace D. Wong.
S.M.
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Schlutz, Matthew. "Synthetic Aperture Radar Imaging Simulated in MATLAB." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/92.
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This thesis further develops a method from ongoing thesis projects with the goal of generating images using synthetic aperture radar (SAR) simulations coded in MATLAB. The project is supervised by Dr. John Saghri and sponsored by Raytheon Space and Airborne Systems. SAR is a type of imaging radar in which the relative movement of the antenna with respect to the target is utilized. Through the simultaneous processing of the radar reflections over the movement of the antenna via the range Doppler algorithm (RDA), the superior resolution of a theoretical wider antenna, termed synthetic aperture, is obtained. The long term goal of this ongoing project is to develop a simulation in which realistic SAR images can be generated and used for SAR Automatic Target Recognition (ATR). Current and past Master’s theses on ATR were restricted to a small data set of Man-portable Surveillance and Target Acquisition Radar (MSTAR) images as most SAR images for military ATR are not released for public use. Also, with an in-house SAR image generation scheme the parameters of noise, target orientation, the elevation angle or look angle to the antenna from the target and other parameters can be directly controlled and modified to best serve ATR purposes or other applications such as three-dimensional SAR holography.
At the start of the project in September 2007, the SAR simulation from previous Master’s theses was capable of simulating and imaging point targets in a two dimensional plane with limited mobility. The focus on improvements to this simulation through the course of this project was to improve the SAR simulation for applications to more complex two-dimensional targets and simple three-dimensional targets, such as a cube. The input to the simulation uses a selected two-dimensional, grayscale target image and generates from the input a two-dimensional target profile of reflectivity over the azimuth and range based on the intensity of the pixels in the target image. For three-dimensional simulations, multiple two-dimensional azimuth/range profiles are imported at different altitudes. The output from both the two-dimensional and three-dimensional simulations is the SAR simulated and RDA processed image of the input target profile.
Future work on this ongoing project will include an algorithm to calculate line of sight limitations of point targets and processing optimization of the radar information generation implemented in the code so that more complex and realistic targets can be simulated and imaged using SAR for applications in ATR and 3D SAR holography.
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Laubie, Ellen. "Aspect Diversity for Bistatic Synthetic Aperture Radar." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1492420649395159.
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Wray, Lisa Shannon. "Synthetic aperture radar image simulator for interferometry." Master's thesis, University of Cape Town, 2001. http://hdl.handle.net/11427/5078.
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Bibliography: leaves 111-113.An interferometric synthetic aperture radar (SAR) simulator was created for the purposes of experimenting with and demonstration of the interferometric process, mission planning and radar image interpretation. The simulation method employs image statistics and terrain geometry to form a synthetic image and requires inputs of a digital elevation model (DEM), flight path, description, radar parameters, a terrain classification map and temporal decorrelation factors. Output images include the following images: radar cross section, power, total coherence, temporal cohernece factor, geometrical coherence factor, absolute phase, interferograms and flattened interferograms.
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Akyildiz, Yeliz. "Feature extraction from synthetic aperture radar imagery." Connect to resource, 2000. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1258651629.
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Liu, Jun. "Ionospheric effects on synthetic aperture radar imaging /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/5890.
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Duncan, David P. "Motion Compensation of Interferometric Synthetic Aperture Radar." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd477.pdf.
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Garcia, Frank W. "Sea ice classification using synthetic aperture radar." Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA232248.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, June 1990.Thesis Advisor(s): Nystuen, J.A. ; Bourke, R.H. "June 1990." "MPS-68-90-004." Description based on title screen as viewed on March 24, 2010. DTIC Identifier(s): Radar Images, Sea Ice, Marginal Ice Zones, Ice Classification, Statistical Analysis, Gray Scale, Odden Ice, Theses. Author(s) subject terms: Synthetic Aperture Radar, Sea Ice Classification, Marginal Zone, Gray Level Co-Occurrence Matrices, Texture Statistics, Univariate Statistics, MIZEX '87 SAR Data. Includes bibliographical references (p. 96-98). Also available in print.
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Knight, Chad P. "Convex Model-Based Synthetic Aperture Radar Processing." DigitalCommons@USU, 2014. https://digitalcommons.usu.edu/etd/2340.
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The use of radar often conjures up images of small blobs on a screen. But current synthetic aperture radar (SAR) systems are able to generate near-optical quality images with amazing benefits compared to optical sensors. These SAR sensors work in all weather conditions, day or night, and provide many advanced capabilities to detect and identify targets of interest. These amazing abilities have made SAR sensors a work-horse in remote sensing, and military applications. SAR sensors are ranging instruments that operate in a 3D environment, but unfortunately the results and interpretation of SAR images have traditionally been done in 2D. Three-dimensional SAR images could provide improved target detection and identification along with improved scene interpretability. As technology has increased, particularly regarding our ability to solve difficult optimization problems, the 3D SAR reconstruction problem has gathered more interest. This dissertation provides the SAR and mathematical background required to pose a SAR 3D reconstruction problem. The problem is posed in a way that allows prior knowledge about the target of interest to be integrated into the optimization problem when known. The developed model is demonstrated on simulated data initially in order to illustrate critical concepts in the development. Then once comprehension is achieved the processing is applied to actual SAR data. The 3D results are contrasted against the current gold- standard. The results are shown as 3D images demonstrating the improvement regarding scene interpretability that this approach provides.
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West, Roger D. "Model-Based Stripmap Synthetic Aperture Radar Processing." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/962.
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Synthetic aperture radar (SAR) is a type of remote sensor that provides its own illumination and is capable of forming high resolution images of the reflectivity of a scene. The reflectivity of the scene that is measured is dependent on the choice of carrier frequency; different carrier frequencies will yield different images of the same scene.
There are different modes for SAR sensors; two common modes are spotlight mode and stripmap mode. Furthermore, SAR sensors can either be continuously transmitting a signal, or they can transmit a pulse at some pulse repetition frequency (PRF). The work in this dissertation is for pulsed stripmap SAR sensors.
The resolvable limit of closely spaced reflectors in range is determined by the bandwidth of the transmitted signal and the resolvable limit in azimuth is determined by the bandwidth of the induced azimuth signal, which is strongly dependent on the length of the physical antenna on the SAR sensor. The point-spread function (PSF) of a SAR system is determined by these resolvable limits and is limited by the physical attributes of the SAR sensor.
The PSF of a SAR system can be defined in different ways. For example, it can be defined in terms of the SAR system including the image processing algorithm. By using this definition, the PSF is an algorithm-specific sinc-like function and produces the bright, star-like artifacts that are noticeable around strong reflectors in the focused image. The PSF can also be defined in terms of just the SAR system before any image processing algorithm is applied. This second definition of the PSF will be used in this dissertation. Using this definition, the bright, algorithm-specific, star-like artifacts will be denoted as the inter-pixel interference (IPI) of the algorithm. To be specific, the combined effect of the second definition of PSF and the algorithm-dependent IPI is a decomposition of the first definition of PSF.
A new comprehensive forward model for stripmap SAR is derived in this dissertation. New image formation methods are derived in this dissertation that invert this forward model and it is shown that the IPI that corrupts traditionally processed stripmap SAR images can be removed. The removal of the IPI can increase the resolvability to the resolution limit, thus making image analysis much easier.
SAR data is inherently corrupted by uncompensated phase errors. These phase errors lower the contrast of the image and corrupt the azimuth processing which inhibits proper focusing (to the point of the reconstructed image being unusable). If these phase errors are not compensated for, the images formed by system inversion are useless, as well. A model-based autofocus method is also derived in this dissertation that complements the forward model and corrects these phase errors before system inversion.
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Shoalehvar, Amin. "Synthetic Aperture Radar (SAR) Raw Signal Simulation." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/755.
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Abstract
Synthetic Aperture Radar (SAR) Raw Signal Simulation
Author: Amin Shoalehvar
Synthetic aperture radar (SAR) raw signal simulation is a useful tool for SAR system design, mission planning, processing algorithm testing, and inversion algorithm design. This thesis explores a SAR raw signal simulation. The raw signal simulation is the simulated received signal before any processing with exception of the down-converter. The simulation plays a significant role in studies concerning noise and clutter rejection and contributes toward optimizing SAR system parameters. To simulate SAR raw data, a Chirp Scaling (CS) method is used. This method [3] first stretches the input surface reflectivity of the target in the azimuth and range direction respectively. Then it derives the raw data by inverse equalizing the signal based on CS principle. This method avoids the time-domain integral operation and improves the computational efficiency. A simulation diagram, calculation and systematic process are proposed in this thesis. Finally, simulation results are presented to verify the accuracy of calculations and the efficiency of the process.