Relevant bibliographies by topics / Polarimetric Synthetic Aperture Radar

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Academic literature on the topic 'Polarimetric Synthetic Aperture Radar'

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Published: 25 May 2024

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Journal articles on the topic "Polarimetric Synthetic Aperture Radar"

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Raney, R. Keith. "Hybrid Dual-Polarization Synthetic Aperture Radar." Remote Sensing 11, no. 13 (June 27, 2019): 1521. http://dx.doi.org/10.3390/rs11131521.

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Compact polarimetry for a synthetic aperture radar (SAR) system is reviewed. Compact polarimetry (CP) is intended to provide useful polarimetric image classifications while avoiding the disadvantages of space-based quadrature-polarimetric (quad-pol) SARs. Two CP approaches are briefly described, π/4 and circular. A third form, hybrid compact polarimetry (HCP) has emerged as the preferred embodiment of compact polarimetry. HCP transmits circular polarization and receives on two orthogonal linear polarizations. When seen through its associated data processing and image classification algorithms, HPC’s heritage dates back to the Stokes parameters (1852), which are summarized and explained in plain language. Hybrid dual-polarimetric imaging radars were in the payloads of two lunar-orbiting satellites, India’s Earth-observing RISAT-1, and Japan’s ALOS-2. In lunar or planetary orbit, a satellite equipped with an HCP imaging radar delivers the same class of polarimetric information as Earth-based radar astronomy. In stark contrast to quad-pol, compact polarimetry is compatible with wide swath modes of a SAR, including ScanSAR. All operational modes of the SARs aboard Canada’s three-satellite Radarsat Constellation Mission (RCM) are hybrid dual-polarimetric. Image classification methodologies for HCP data are reviewed, two of which introduce errors for reasons explained. Their use is discouraged. An alternative and recommended group of methodologies yields reliable results, illustrated by polarimetrically classified images. A survey over numerous quantitative studies demonstrates HCP polarimetric classification effectiveness. The results verify that the performance accuracy of the HCP architecture is comparable to the accuracy delivered by a quadrature-polarized SAR. Four appendices are included covering related topics, including comments on inflight calibration of an HCP radar.
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Novak, L. M., and C. M. Netishen. "Polarimetric synthetic aperture radar imaging." International Journal of Imaging Systems and Technology 4, no. 4 (1992): 306–18. http://dx.doi.org/10.1002/ima.1850040410.

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El Assad, S., X. Morin, D. Barba, and V. Slavova. "Compression of Polarimetric Synthetic Aperture Radar Data." Progress In Electromagnetics Research 39 (2003): 125–45. http://dx.doi.org/10.2528/pier02053002.

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Rignot, E., and R. Chellappa. "Segmentation of polarimetric synthetic aperture radar data." IEEE Transactions on Image Processing 1, no. 3 (July 1992): 281–300. http://dx.doi.org/10.1109/83.148603.

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Thakur, P. K., R. D. Garg, S. P. Aggarwal, P. K. Garg, and J. Shi. "Snow density retrieval using SAR data: algorithm validation and applications in part of North Western Himalaya." Cryosphere Discussions 7, no. 3 (May 3, 2013): 1927–60. http://dx.doi.org/10.5194/tcd-7-1927-2013.

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Abstract. The current study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the dry snow density in Manali sub-basin of Beas River located in state of Himachal Pradesh, India. SAR data from Radarsat-2 (RS2), Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) have been used. The SAR based inversion models were implemented separately for fully polarimetric RS2, PALSAR and dual polarimetric ASAR Alternate polarization System (APS) datasets in Mathematica and MATLAB software and have been used for finding out dry snow dielectric constant and snow density. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. Overall accuracy in terms of R2 value and Root Mean Square Error (RMSE) was calculated as 0.85 and 0.03 g cm−3 for snow density based on the ground truth data. The retrieved snow density is highly useful for snow avalanche and snowmelt runoff modeling related studies of this region.
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Ramana, K. V., P. Srikanth, U. Deepika, and M. V. R. Sesha Sai. "Polarimetric Synthetic Aperture Radar data for Crop Cover Classification." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 117–21. http://dx.doi.org/10.5194/isprsannals-ii-8-117-2014.

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The interest in crop inventory through the use of microwave sensors is on the rise owing to need for accurate crop forecast and the availability of multi polarization data. Till recently, the temporal amplitude data has been used for crop discrimination as well as acreage estimation. With the availability of dual and quadpol data, the differential response of crop geometry at various crop growth stages to various polarizations is being exploited for discrimination and classification of crops. An attempt has been made in the current study with RISAT1 and Radarsat2 C-band single, dual, fully and hybrid polarimetric data for crop inventory. The single date hybrid polarimetric data gave comparable results to the three date single polarization data as well as with the single date fully polarimetric data for crops like rice and cotton.
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Adil, Muhammad, Andrea Buono, Ferdinando Nunziata, Emanuele Ferrentino, Domenico Velotto, and Maurizio Migliaccio. "On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship." Remote Sensing 14, no. 22 (November 17, 2022): 5813. http://dx.doi.org/10.3390/rs14225813.

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The monitoring of ships is of paramount importance for ocean and coastal area surveillance. The synthetic aperture radar is shown to be a key sensor to provide effective and continuous observation of ships due to its unique imaging capabilities. When advanced synthetic aperture radar imaging systems are considered, the full scattering information is available that was demonstrated to be beneficial in developing improved ship detection and classification algorithms. Nonetheless, the capability of polarimetric synthetic aperture radar to observe marine vessels is significantly affected by several imaging and environmental parameters, including the incidence angle. Nonetheless, how changes in the incidence angle affect the scattering of ships still needs to be further investigated since only a sparse analysis, i.e., on different kinds of ships of different sizes observed at multiple incidence angles, has been performed. Hence, in this study, for the first time, the polarimetric scattering of the same ship, i.e., a small fishing trawler, which is imaged multiple times under the same sea state conditions but in a wide range of incidence angles, is analysed. This unique opportunity is provided by a premium L-band UAVSAR airborne dataset that consists of five full-polarimetric synthetic aperture radar scenes collected in the Gulf of Mexico. Experimental results highlight the key role played by the incidence angle on both coherent, i.e., co-polarisation signature and pedestal height, and incoherent, i.e., multi-polarisation and total backscattering power, polarimetric scattering descriptors. Experimental results show that: (1) the polarised scattering component is more sensitive to the incidence angle with respect to the unpolarised one; (2) the co-polarised channel under horizontal polarisation dominated the polarimetric backscattering from the fishing trawler at lower angles of incidence, while both co-polarised channels contribute to the polarimetric backscattering at higher incidence angles; (3) the HV polarisation provides the largest target-to-clutter ratio at lower incidence angles, while the HH polarisation should be preferred at higher angles of incidence.
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Nord, M. E., M. E. Nord, T. L. Ainsworth, Jong-Sen Lee, and N. J. S. Stacy. "Comparison of Compact Polarimetric Synthetic Aperture Radar Modes." IEEE Transactions on Geoscience and Remote Sensing 47, no. 1 (January 2009): 174–88. http://dx.doi.org/10.1109/tgrs.2008.2000925.

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Ertin, E., and L. C. Potter. "Polarimetric calibration for wideband synthetic aperture radar imaging." IEE Proceedings - Radar, Sonar and Navigation 145, no. 5 (1998): 275. http://dx.doi.org/10.1049/ip-rsn:19982224.

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Hauter, Andrew, Kuo Chu Chang, and Sherman Karp. "Polarimetric fusion for synthetic aperture radar target classification." Pattern Recognition 30, no. 5 (May 1997): 769–75. http://dx.doi.org/10.1016/s0031-3203(96)00099-4.

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Dissertations / Theses on the topic "Polarimetric Synthetic Aperture Radar"

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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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Books on the topic "Polarimetric Synthetic Aperture Radar"

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Hajnsek, Irena, and Yves-Louis Desnos, eds. Polarimetric Synthetic Aperture Radar. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6.

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Zyl, Jakob Van. Synthetic aperture radar polarimetry. Hoboken, NJ: Wiley, 2011.

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van Zyl, Jakob, and Yunjin Kim. Synthetic Aperture Radar Polarimetry. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118116104.

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Chen, Si-Wei, Xue-Song Wang, Shun-Ping Xiao, and Motoyuki Sato. Target Scattering Mechanism in Polarimetric Synthetic Aperture Radar. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7269-7.

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Italy), POLinSAR (2003 Frascati. Proceedings of the workshop POLinSAR, Applications of SAR polarimetry and polarimetric interferometry: 14-16 January 2003, Frascati, Italy. Edited by Sawaya-Lacoste Huguette and European Space Agency. Noordwijk, The Netherlands: ESA Publications, 2003.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Synthetic aperture radar imagery of airports and surrounding areas: Study of clutter at grazing angles and their polarimetric properties. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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Inverse synthetic aperture radar imaging: Principles, algorithms, and applications. Edison, NJ: Scitech Publishing, an imprint of the IET, 2014.

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Marino, Armando. A New Target Detector Based on Geometrical Perturbation Filters for Polarimetric Synthetic Aperture Radar (POL-SAR). Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27163-2.

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Marino, Armando. A New Target Detector Based on Geometrical Perturbation Filters for Polarimetric Synthetic Aperture Radar (POL-SAR). Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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JPL Airborne Earth Science Workshop (6th 1996 Pasadena, Calif.). Summaries of the sixth annual JPL Airborne Earth Science Workshop, March 4-8, 1996. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1996.

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Book chapters on the topic "Polarimetric Synthetic Aperture Radar"

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López-Martínez, C., and E. Pottier. "Basic Principles of SAR Polarimetry." In Polarimetric Synthetic Aperture Radar, 1–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_1.

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AbstractThis chapter critically summarizes the main theoretical aspects necessary for a correct processing and interpretation of the polarimetric information towards the development of applications of synthetic aperture radar (SAR) polarimetry. First of all, the basic principles of wave polarimetry (which deals with the representation and the understanding of the polarization state of an electromagnetic wave) and scattering polarimetry (which concerns inferring the properties of a target given the incident and the scattered polarized electromagnetic waves) are given. Then, concepts regarding the description of polarimetric data are reviewed, covering statistical and scattering aspects, the latter in terms of coherent and incoherent decomposition techniques. Finally, polarimetric SAR interferometry and tomography, two acquisition modes that enable the extraction of the 3-D scatterer position and separation, respectively, and their polarimetric characterization, are described.
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Lopez-Sanchez, J. M., J. D. Ballester-Berman, F. Vicente-Guijalba, S. R. Cloude, H. McNairn, J. Shang, H. Skriver, et al. "Agriculture and Wetland Applications." In Polarimetric Synthetic Aperture Radar, 119–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_3.

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AbstractBased on experimental results, this chapter describes applications of SAR polarimetry to extract relevant information on agriculture and wetland scenarios by exploiting differences in the polarimetric signature of different scatterers, crop types and their development stage depending on their physical properties. Concerning agriculture, crop type mapping, soil moisture estimation and phenology estimation are reviewed, as they are ones with a clear benefit of full polarimetry over dual or single polarimetry. For crop type mapping, supervised or partially unsupervised classification schemes are used. Phenology estimation is treated as a classification problem as well, by regarding the different stages as different classes. Soil moisture estimation makes intensive use of scattering models, in order to separate soil and vegetation scattering and to invert for soil moisture from the isolated ground component. Then, applications of SAR polarimetry to wetland monitoring are considered that include the delineation of their extent and their characterisation by means of polarimetric decompositions. In the last section of the chapter, the use of a SAR polarimetric decomposition is shown for the assessment of the damages consequential to earthquakes and tsunamis.
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Papathanassiou, K. P., S. R. Cloude, M. Pardini, M. J. Quiñones, D. Hoekman, L. Ferro-Famil, D. Goodenough, et al. "Forest Applications." In Polarimetric Synthetic Aperture Radar, 59–117. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_2.

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AbstractThe application of polarimetric Synthetic Aperture Radar (SAR) to forest observation for mapping, classification and parameter estimation (especially biomass) has a relatively long history. The radar penetration through forest volume, and hence the multi-layer nature of scattering models, make fully polarimetric data the observation space enabling a robust and full inversion of such models. A critical advance came with the introduction of polarimetric SAR interferometry, where polarimetry provides the parameter diversity, while the interferometric baseline proves a user-defined entropy control as well as spatial separation of scattering components, together with their location in the third dimension (height). Finally, the availability of multiple baselines leads to the full 3-D imaging of forest volumes through TomoSAR, the quality of which is again greatly enhanced by the inclusion of polarimetry. The objective of this Chapter is to review applications of SAR polarimetry, polarimetric interferometry and tomography to forest mapping and classification, height estimation, 3-D structure characterization and biomass estimation. This review includes not only models and algorithms, but it also contains a large number of experimental results in different test sites and forest types, and from airborne and space borne SAR data at different frequencies.
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Colin-Koeniguer, E., N. Trouve, Y. Yamaguchi, Y. Huang, L. Ferro-Famil, V. D. Navarro Sanchez, J. M. Lopez Sanchez, et al. "Urban Applications." In Polarimetric Synthetic Aperture Radar, 215–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_5.

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AbstractThe experimental result reported in this chapter review the application of (high resolution) Synthetic Aperture Radar (SAR) data to extract valuable information for monitoring urban environments in space and time. Full polarimetry is particularly useful for classification, as it allows the detection of built-up areas and to discriminate among their different types exploiting the variation of the polarimetric backscatter with the orientation, shape, and distribution of buildings and houses, and street patterns. On the other hand, polarimetric SAR data acquired in interferometric configuration can be combined for 3-D rendering through coherence optimization techniques. If multiple baselines are available, direct tomographic imaging can be employed, and polarimetry both increases separation performance and characterizes the response of each scatterer. Finally, polarimetry finds also application in differential interferometry for subsidence monitoring, for instance, by improving both the number of resolution cells in which the estimate is reliable, and the quality of these estimates.
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Hajnsek, I., G. Parrella, A. Marino, T. Eltoft, M. Necsoiu, L. Eriksson, and M. Watanabe. "Cryosphere Applications." In Polarimetric Synthetic Aperture Radar, 179–213. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_4.

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AbstractSynthetic aperture radar (SAR) provides large coverage and high resolution, and it has been proven to be sensitive to both surface and near-surface features related to accumulation, ablation, and metamorphism of snow and firn. Exploiting this sensitivity, SAR polarimetry and polarimetric interferometry found application to land ice for instance for the estimation of wave extinction (which relates to sub surface ice volume structure) and for the estimation of snow water equivalent (which relates to snow density and depth). After presenting these applications, the Chapter proceeds by reviewing applications of SAR polarimetry to sea ice for the classification of different ice types, the estimation of thickness, and the characterisation of its surface. Finally, an application to the characterisation of permafrost regions is considered. For each application, the used (model-based) decomposition and polarimetric parameters are critically described, and real data results from relevant airborne campaigns and space borne acquisitions are reported.
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Migliaccio, M., F. Nunziata, A. Marino, C. Brekke, and S. Skrunes. "Ocean Applications." In Polarimetric Synthetic Aperture Radar, 255–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_6.

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AbstractIn this chapter, the most promising techniques to observe oil slicks and to detect metallic targets at sea using polarimetric synthetic aperture radar (SAR) data are reviewed and critically analysed. The detection of oil slicks in SAR data is made difficult not only by the presence of speckle but also by the presence of, e.g. biogenic films, low-wind areas, rain cells, currents, etc., which increase the false alarm probability. The use of polarimetric features has been shown to both observe oil slicks and distinguish them from weak-damping look-alikes but also to extract some of their properties. Similarly to oil slicks, the same factors can hamper the detection of metallic targets at sea. The radiometric information provided by traditional single-channel SAR is not generally sufficient to unambiguously detect man-made metallic targets over the sea surface. This shortcoming is overcome by employing polarimetry, which allows to fully characterize the scattering mechanism of such targets.
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Yang, Ruliang, Bowei Dai, Lulu Tan, Xiuqing Liu, Zhen Yang, and Haiying Li. "Polarimetric Synthetic Aperture Radar." In Polarimetric Microwave Imaging, 75–122. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8897-6_3.

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Yang, Ruliang, Bowei Dai, Lulu Tan, Xiuqing Liu, Zhen Yang, and Haiying Li. "Polarimetric Interferometric Synthetic Aperture Radar." In Polarimetric Microwave Imaging, 123–43. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8897-6_4.

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Shafai, Shahid Shuja, Shashi Kumar, Hossein Aghababaei, and Anurag Kulshrestha. "Polarimetric Interferometric Decomposition." In Spaceborne Synthetic Aperture Radar Remote Sensing, 45–87. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003204466-3.

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Marino, Armando. "Synthetic Aperture Radar." In A New Target Detector Based on Geometrical Perturbation Filters for Polarimetric Synthetic Aperture Radar (POL-SAR), 9–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27163-2_2.

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Conference papers on the topic "Polarimetric Synthetic Aperture Radar"

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WELSH, R., D. ANDRE, and M. FINNIS. "LABORATORY MULTISTATIC POLARIMETRIC SPARSE APERTURE 3D SAR INVESTIGATION." In Synthetic Aperture Sonar and Synthetic Aperture Radar 2023. Institute of Acoustics, 2023. http://dx.doi.org/10.25144/15941.

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Zhou, Zhengshu, Kenji Takasawa, and Motoyuki Sato. "Interferometric polarimetric synthetic aperture radar system." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441426.

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Xu, Feng, Yongchen Li, and Ya-Qiu Jin. "Polarimetric-anisotropic decomposition of synthetic aperture radar." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730957.

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Sotirelis, Paul, Sean Gilmore, and Adam Nolan. "Using deep learning to estimate linear structure orientation in polarimetric radar data." In Algorithms for Synthetic Aperture Radar Imagery XXVII, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2020. http://dx.doi.org/10.1117/12.2561575.

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Sato, Ryoichi, Toshifum Moriyama, Yuya Arima, Yoshio Yamaguchi, Hiroyoshi Yamada, Ryu Sugimoto, Chiaki Tsutsumi, and Ryosuke Nakamura. "Fundamental Polarimetric Scattering Analysis For Detecting Oriented Manmade Objects Using Polarimetric Correlation Coefficients." In 2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2023. http://dx.doi.org/10.1109/apsar58496.2023.10388936.

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Chen, Honglei, and Dayalan Kasilingam. "Super-Resolution Processing for Polarimetric Synthetic Aperture Radar Tomography." In 2007 IEEE Radar Conference. IEEE, 2007. http://dx.doi.org/10.1109/radar.2007.374290.

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Liu, Bin, Hao Hu, Huanyu Wang, Kaizhi Wang, Xingzhao Liu, and Wenxian Yu. "Superpixel-based classification of polarimetric synthetic aperture radar images." In 2011 IEEE Radar Conference (RadarCon). IEEE, 2011. http://dx.doi.org/10.1109/radar.2011.5960609.

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Zhou Yong-sheng, Hong Wen, Wang Yan-Ping, Wu Yi-rong, and Cao Fang. "Baseline analysis of Polarimetric SAR Interferometry." In 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar. IEEE, 2007. http://dx.doi.org/10.1109/apsar.2007.4418584.

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Zhu Yongtao, Wang Caiyun, Li Liang, and Hong Jun. "An X band polarimetric ARC prototype." In 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar. IEEE, 2007. http://dx.doi.org/10.1109/apsar.2007.4418593.

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Cui, Xing-Chao, Chen-Song Tao, Si-Wei Chen, and Yi Su. "PolSAR Ship Detection with Polarimetric Correlation Pattern." In 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2019. http://dx.doi.org/10.1109/apsar46974.2019.9048310.

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Reports on the topic "Polarimetric Synthetic Aperture Radar"

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Ralston, James M., and Elizabeth L. Ayers. Antenna Effects on Polarimetric Imagery in Ultrawide Synthetic Aperture Radar. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada415541.

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Lukowski, T. I., B. Yue, and K. E. Mattar. Synthetic Aperture Radar for search and rescue: polarimetry and interferometry. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/220094.

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DICKEY, FRED M., LOUIS ROMERO, and ARMIN W. DOERRY. Superresolution and Synthetic Aperture Radar. Office of Scientific and Technical Information (OSTI), May 2001. http://dx.doi.org/10.2172/782711.

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Boverie, B., B. C. Brock, and A. W. Doerry. Soil-penetrating synthetic aperture radar. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/10123330.

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Doerry, Armin Walter. Performance limits for Synthetic Aperture Radar. Office of Scientific and Technical Information (OSTI), February 2006. http://dx.doi.org/10.2172/878591.

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Doerry, Armin W. Motion measurement for synthetic aperture radar. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1167411.

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Doerry, A. W. Performance Limits for Synthetic Aperture Radar. Office of Scientific and Technical Information (OSTI), January 2001. http://dx.doi.org/10.2172/773988.

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Doerry, Armin, and Douglas Bickel. Synthetic Aperture Radar Height of Focus. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1761028.

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Doerry, A. W. Synthetic aperture radar processing with tiered subapertures. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/10161315.

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Walker, David T. Wave-Coherence Measurements Using Synthetic-Aperture Radar. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada610000.

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