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Relevant bibliographies by topics / Remote Sensing Image Data Analysis

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Academic literature on the topic 'Remote Sensing Image Data Analysis'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Remote Sensing Image Data Analysis"

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Fu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.

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Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.

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Li, Runya, and Shenglian Li. "Multimedia Image Data Analysis Based on KNN Algorithm." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/7963603.

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In order to improve the authenticity of multispectral remote sensing image data analysis, the KNN algorithm and hyperspectral remote sensing technology are used to organically combine advanced multimedia technology with spectral technology to subdivide the spectrum. Different classification methods are used to classify CHRIS 0°, and the results are analyzed and compared: SVM classification accuracy is the highest 72 8448%, Kappa coefficient is 0.6770, and SVM is used to classify CHRIS images from five angles, and the results are compared and analyzed: the classification accuracy is from high to low, and the order is FZA = 0 > FZA = −36 > FZA = −55 > FZA = 36 > FZA = 55; SVM is used to classify the multiangle combined image, and the result is compared with the CHRIS 0° result: the overall classification accuracy of angle-combined image types is lower than that of single-angle images; the SVM is used to classify the band-combined image, and the result is compared with CHRIS 0°: the overall classification accuracy of the band combination image forest type is very low, and the effect is not as good as the combining multiangle image classification results. It is verified that if CHRIS multiangle hyper-spectral data are used for classification, the SVM method should be used to classify spectral remote sensing image data with the best effect.

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Karimov, B., G. Karimova, and N. Amankulova. "Land Cover Classification Improvements by Remote Sensing Data Fusion." Bulletin of Science and Practice, no. 2 (February 15, 2023): 66–74. http://dx.doi.org/10.33619/2414-2948/87/07.

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Computer processing and analysis of satellite data is an urgent task of the science of remote sensing of the earth. Such processing can range from adjusting the contrast and brightness of the images of an amateur photographer to a group of scientists using neural network classification to determine the types of minerals in a hyperspectral satellite image. This article implements a method of satellite data fusion, which improves the digital image interpretation and image quality for further analysis. For fusion, a multispectral image with a resolution of 30 m Landsat 5 with 6 channels was taken, with three more significant and informative in their composition were used, as well as a panchromatic (monochrome) image with a resolution of 15 m. To evaluate the resolution of the images and the resulting images before and after the image fusion algorithm, image slices along a straight line and intersecting buildings, green mass, roads and industrial areas presented. For testing, test territories taken from Google Earth and the field work results.

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Veljanovski, Tatjana, Urša Kanjir, and Krištof Oštir. "Object-based image analysis of remote sensing data." Geodetski vestnik 55, no. 04 (2011): 641–64. http://dx.doi.org/10.15292/geodetski-vestnik.2011.04.641-664.

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Bazi, Yakoub, Gabriele Cavallaro, Begüm Demir, and Farid Melgani. "Learning from Data for Remote Sensing Image Analysis." International Journal of Remote Sensing 43, no. 15-16 (August 18, 2022): 5527–33. http://dx.doi.org/10.1080/01431161.2022.2131481.

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Lukáš Brodský and Luboš, Borůvka. "Object-oriented Fuzzy Analysis of Remote Sensing Data for Bare Soil Brightness Mapping." Soil and Water Research 1, No. 3 (January 7, 2013): 79–84. http://dx.doi.org/10.17221/6509-swr.

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Remote sensing data have an important advantage; the data provide spatially exhaustive sampling of the area of interest instead of having samples of tiny fractions. Vegetation cover is, however, one of the application constraints in soil science. Areas of bare soil can be mapped. These spatially dense data require proper techniques to map identified patterns. The objective of this study was mapping of spatial patterns of bare soil colour brightness in a Landsat 7 satellite image in the study area of Central Bohemia using object-oriented fuzzy analysis. A soil map (1:200 000) was used to associate soil types with the soil brightness in the image. Several approaches to determine membership functions (MF) of the fuzzy rule base were tested. These included a simple manual approach, k-means clustering, a method based on the sample histogram, and one using the probability density function. The method that generally provided the best results for mapping the soil brightness was based on the probability density function with KIA = 0.813. The resulting classification map was finally compared with an existing soil map showing 72.0% agreement of the mapped area. The disagreement of 28.0% was mainly in the areas of Chernozems (69.3%).

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Yu, Songyi, and Guotao Wang. "Study on the example segmentation method of remote sensing image based on neural network." Advances in Engineering Technology Research 6, no. 1 (June 12, 2023): 129. http://dx.doi.org/10.56028/aetr.6.1.129.2023.

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With the rapid development of modern science and technology, people collect remote sensing data from the altitude perspective at the same time, put forward the use of a variety of remote sensing images to solve the military exploration, meteorological analysis, environmental protection, resource exploration and other basic problems. However, since remote sensing images have the characteristics of too large data, high image resolution and extremely low application efficiency, some scholars have used the image features in residual network problems in their research to solve the problem of remote sensing image target segmentation scale difference based on the attention mechanism and single-step case segmentation framework. In this paper, based on the understanding of the research status of neural networks and remote sensing image application, a remote sensing image segmentation model based on multi-level channel attention is proposed according to the model architecture of convolutional neural networks. The final experimental results show that the neural network based remote sensing image case segmentation technology has positive effects.

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Khare, Smriti. "Remote Sensing Imagery Sensors and Image Interpretation." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 606–7. http://dx.doi.org/10.22214/ijraset.2021.38019.

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Abstract: Remote sensing a universal term that represents the activity of gaining data of an object with a sensor that is genuinely away from the item from an aircraft or satellite. Special cameras are used to gather remotely sensed picture which help the analyst to sense the things about the earth. Remote sensing makes it probable to assemble data of risky or unapproachable zones. Remote sensing data allows researchers to examine the biosphere's biotic and abiotic segments. Remote sensing is used in various fields to acquire the data which is widely used in Geographical Information System. Image interpretation is most basic feature of remote sensing technology. Image interpretation is a process of recognizing the images and collect information for multiple uses. The photographs are usually taken by satellite or aircrafts. Keywords: Image interpretation, image interpretation devices, sensor, remote sensing, data analysis.

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Hutapea, Destri Yanti, and Octaviani Hutapea. "WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING." International Journal of Remote Sensing and Earth Sciences (IJReSES) 15, no. 1 (July 6, 2018): 63. http://dx.doi.org/10.30536/j.ijreses.2018.v15.a2824.

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Remote sensing satellite imagery is currently needed to support the needs of information in various fields. Distribution of remote sensing data to users is done through electronic media. Therefore, it is necessary to make security and identity on remote sensing satellite images so that its function is not misused. This paper describes a method of adding confidential information to medium resolution remote sensing satellite images to identify the image using steganography technique. Steganography with the Least Significant Bit (LSB) method is chosen because the insertion of confidential information on the image is performed on the rightmost bits in each byte of data, where the rightmost bit has the smallest value. The experiment was performed on three Landsat 8 images with different area on each composite band 4,3,2 (true color) and 6,5,3 (false color). Visually the data that has been inserted information does not change with the original data. Visually, the image that has been inserted with confidential information (or stego image) is the same as the original image. Both images cannot be distinguished on histogram analysis. The Mean Squared Error value of stego images of all three data less than 0.053 compared with the original image. This means that information security with steganographic techniques using the ideal LSB method is used on remote sensing satellite imagery.

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Tang, Yang, Jiongchao Yan, Yueqi Wu, Jie Hong, Lei Xu, and Zhangrui Lin. "Design of Remote Sensing Image Data Analysis and Processing Platform Based on Environmental Monitoring." Journal of Physics: Conference Series 2136, no. 1 (December 1, 2021): 012056. http://dx.doi.org/10.1088/1742-6596/2136/1/012056.

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Abstract In the continuous innovation of modern technology concept, remote sensing technology as an advanced and practical comprehensive detection technology has been widely used in many fields. Especially for environmental monitoring, the rational use of remote sensing image data analysis and processing platform can not only obtain valuable environmental information, but also provide effective management decisions for climate changeable natural disasters and other issues. Therefore, on the basis of understanding the design scheme of remote sensing image data analysis and processing platform system, this paper makes clear the positive role of remote sensing image processing technology in the development of environmental monitoring based on the application of the platform.

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Dissertations / Theses on the topic "Remote Sensing Image Data Analysis"

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Fountanas, Leonidas. "Principal components based techniques for hyperspectral image data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FFountanas.pdf.

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Cheriyadat, Anil Meerasa. "Limitations of principal component analysis for dimensionality-reduction for classification of hyperspectral data." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-11072003-133109.

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Robert, Denis J. "Selection and analysis of optimal textural features for accurate classification of monochrome digitized image data /." Online version of thesis, 1989. http://hdl.handle.net/1850/11364.

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KHALIQ, ALEEM. "Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2839838.

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Marcellin, Michael W., Naoufal Amrani, Serra-Sagristà Joan, Valero Laparra, and Jesus Malo. "Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data." IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016. http://hdl.handle.net/10150/621311.

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A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelettransformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.

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Fischer, Manfred M., Sucharita Gopal, Petra Staufer-Steinnocher, and Klaus Steinocher. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application." WU Vienna University of Economics and Business, 1995. http://epub.wu.ac.at/4184/1/WSG_DP_4695.pdf.

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This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets. (authors' abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience

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Linden, Sebastian van der. "Investigating the potential of hyperspectral remote sensing data for the analysis of urban imperviousness." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15757.

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Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt. Eine detaillierte Ergebnisvalidierung erfolgt mittels vielfältiger Referenzdaten. Es wird gezeigt, dass die Kartengenauigkeit von allen Verarbeitungsschritten abhängt: Support Vector Maschinen klassifizieren Hyperspektraldaten akkurat aber die Kartengenauigkeit wird durch die Georeferenzierung deutlich gemindert; die Versiegelungskartierung stellt die Situation am Boden gut dar, aber die Überdeckung versiegelter Flächen durch Bäume bedingt systematische Fehlschätzungen; eine Bildsegmentierung führt zu keiner Verbesserung der Klassifikationsergebnisse, bietet jedoch eine sinnvolle Möglichkeit zur effektiveren Prozessierung durch Datenkomprimierung. Auf diesem Weg ermöglicht die vorliegende Arbeit Rückschlüsse zur Verlässlichkeit von Datenprodukten, die eine Ausweitung fernerkundlicher Analysen in weniger gut dokumentierte urbane Räume voranbringt.
Urbanization is one of the most powerful and irreversible processes by which humans modify the Earth''s surface. Optical remote sensing is a main source of Earth observation products which help to better understand this dynamic process and its consequences. This work investigates the potential of airborne hyperspectral data to provide information on urban imperviousness that is needed for an integrated analysis of the coupled natural and human systems therein. For this purpose the complete processing workflow from preprocessing of the raw image to the generation of geocoded maps on land cover and impervious surface coverage is performed using Hyperspectral Mapper data acquired over Berlin, Germany. The traditional workflow for hyperspectral data is extended or modified at several points: a normalization of brightness gradients that are caused by directional reflectance properties of urban surfaces is included into radiometric preprocessing; support vector machines are used to classify five spectrally complex land cover classes without previous feature extraction or the definition of sub-classes. A detailed assessment of such maps is performed based on various reference products. Results show that the accuracy of derived maps depends on several steps within the processing workflow. For example, the support vector machine classification of hyperspectral data itself is accurate but geocoding without detailed terrain information introduces critical errors; impervious surface estimates correlate well with ground data but trees covering impervious surface below generally causes offsets; image segmentation does not enhance spectral classification accuracy of the spatially heterogeneous area but offers an interesting way of data compression and more time effective processing. Findings from this work help judging the reliability of data products and in doing so advance a possible extension of urban remote sensing approaches to areas where only little additional data exists.

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Parshakov, Ilia. "Automatic class labeling of classified imagery using a hyperspectral library." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2012, 2012. http://hdl.handle.net/10133/3372.

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Image classification is a fundamental information extraction procedure in remote sensing that is used in land-cover and land-use mapping. Despite being considered as a replacement for manual mapping, it still requires some degree of analyst intervention. This makes the process of image classification time consuming, subjective, and error prone. For example, in unsupervised classification, pixels are automatically grouped into classes, but the user has to manually label the classes as one land-cover type or another. As a general rule, the larger the number of classes, the more difficult it is to assign meaningful class labels. A fully automated post-classification procedure for class labeling was developed in an attempt to alleviate this problem. It labels spectral classes by matching their spectral characteristics with reference spectra. A Landsat TM image of an agricultural area was used for performance assessment. The algorithm was used to label a 20- and 100-class image generated by the ISODATA classifier. The 20-class image was used to compare the technique with the traditional manual labeling of classes, and the 100-class image was used to compare it with the Spectral Angle Mapper and Maximum Likelihood classifiers. The proposed technique produced a map that had an overall accuracy of 51%, outperforming the manual labeling (40% to 45% accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39%), but underperformed compared to the Maximum Likelihood technique (53% to 63%). The newly developed class-labeling algorithm provided better results for alfalfa, beans, corn, grass and sugar beet, whereas canola, corn, fallow, flax, potato, and wheat were identified with similar or lower accuracy, depending on the classifier it was compared with.
vii, 93 leaves : ill., maps (some col.) ; 29 cm

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Magnini, Luigi. "Remote sensing e object-based image analysis: metodologie di approccio per la creazione di standard archeologici." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3423260.

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In recent years, the field of remote sensing experienced an incredible growth thanks to the increasing quality and variety of sensors and the reduction of instrumental costs. The benefits for archaeology were soon apparent. So far, data interpretation remains essentially a prerogative of the human operator and is mediated by his skills and experiences. The continuous increase of datasets volume, i.e. the Big Data Explosion, and the increasing necessity to work on large scale projects require an overall revision of the methods traditionally used in archeology. In this sense, the research presented hereinafter contributes to assess the limits and potential of the emerging field of object-based image analysis (OBIA). The work focused on the definition of OBIA protocols for the treatment of three-dimensional data acquired by airborne and terrestrial laser scanning through the development of a wide range of case studies, used to illustrate the possibilities of the method in archeology. The results include a new, automated approach to identify, map and quantify traces of the First World War landscape around Fort Lusern (Province of Trento, Italy) and the recalcified osteological tissue on the skulls of two burials in the protohistoric necropolis of Olmo di Nogara (Province of Verona, Italy). Moreover, the method was employed to create a predictive model to locate “control places” in mountainous environments; the simulation was built for the Western Asiago Plateau (Province of Vicenza, Italy) and then re-applied with success in basin of Bressanone (Province of Bolzano, Italy). The accuracy of the results was verified thanks to respectively ground surveys, remote cross-validation and comparison with published literature. This confirmed the potential of the methodology, giving reasons to introduce the concept of Archaeological Object-Based Image Analysis (ArchaeOBIA), used to highlight the role of object-based applications in archaeology.
Il campo del remote sensing ha vissuto un incredibile sviluppo negli ultimi anni per merito della crescente qualità e varietà dei sensori e dell’abbattimento dei costi strumentali. Le potenzialità archeologiche sono state ben presto evidenti. Finora, l’interpretazione dei dati è rimasta però prerogativa dell’operatore umano, mediata dalle sue competenze e dalla sua esperienza. Il progressivo aumento di volume dei dataset (cd. “big data explosion”) e la necessità di lavorare su progetti territoriali ad ampia scala hanno reso ora indispensabile una revisione delle modalità di studio tradizionalmente impiegate in ambito archeologico. In questo senso, la ricerca presentata di seguito contribuisce alla valutazione delle potenzialità e dei limiti dell’emergente campo d’indagine dell’object-based image analysis (OBIA). Il lavoro si è focalizzato sulla definizione di protocolli OBIA per il trattamento di dati tridimensionali acquisiti tramite laser scanner aviotrasportato e terrestre attraverso l’elaborazione di un variegato spettro di casi di studio in grado di esemplificare le possibilità offerte dal metodo in archeologia. I risultati ottenuti hanno consentito di identificare, mappare e quantificare in modo automatico e semi-automatico le tracce del paesaggio di guerra nell’area intorno a Forte Luserna (TN) e il tessuto osteologico ricalcificato sui crani di due inumati della necropoli protostorica dell’Olmo di Nogara (VR). Infine, il metodo è stato impiegato per lo sviluppo di un modello predittivo per la localizzazione dei “punti di controllo” in ambiente montano, che è stato studiato per l’area occidentale dell’Altopiano di Asiago (VI) e in seguito riapplicato con successo nella conca di Bressanone (BZ). L’accuratezza dei risultati, verificati di volta in volta tramite ricognizioni a terra, validazione incrociata tramite analisi da remoto e comparazione con i dati editi in letteratura, ha confermato il potenziale della metodologia, consentendo di introdurre il concetto di Archaeological Object-Based Image Analysis (ArchaeOBIA), per rimarcare le specificità delle applicazioni object-based nell’ambito della disciplina archeologica.

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Gapper, Justin J. "Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images." Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/cads_dissertations/2.

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This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean 2) Coral Reef Change Detection in Remote Pacific Islands using Support Vector Machine Classifiers 3) A Generalized Machine Learning Classifier for Spatiotemporal Analysis of Coral Reefs in the Red Sea. The aim of this dissertation is to propose and evaluate a methodology for developing a robust machine learning classifier that can effectively be deployed to accurately detect coral reefs at scale. The hypothesis is that Landsat data can be used to train a classifier to detect coral reefs in remote sensing imagery and that this classifier can be trained to generalize across multiple sites. Another objective is to identify how well different classifiers perform under the generalized conditions and how unique the spectral signature of coral is as environmental conditions vary across observation sites. A methodology for validating the generalization performance of a classifier to unseen locations is proposed and implemented (Controlled Parameter Cross-Validation,). Analysis is performed using satellite imagery from nine different locations with known coral reefs (six Pacific Ocean sites and three Red Sea sites). Ground truth observations for four of the Pacific Ocean sites and two of the Red Sea sites were used to validate the proposed methodology. Within the Pacific Ocean sites, the consolidated classifier (trained on data from all sites) yielded an accuracy of 75.5% (0.778 AUC). Within the Red Sea sites, the consolidated classifier yielded an accuracy of 71.0% (0.7754 AUC). Finally, long-term change detection analysis is conducted for each of the sites evaluated. In total, over 16,700 km2 was analyzed for benthic cover type and cover change detection analysis. Within the Pacific Ocean sites, decreases in coral cover ranged from 25.3% reduction (Kingman Reef) to 42.7% reduction (Kiritimati Island). Within the Red Sea sites, decrease in coral cover ranged from 3.4% (Umluj) to 13.6% (Al Wajh).

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Books on the topic "Remote Sensing Image Data Analysis"

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Processing of remote sensing data. Lisse: Balkema, 2003.

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Girard, Michel-Claude. Processing of remote sensing data. Lisse: Balkema, 2001.

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Canty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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Canty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (2003 Greenbelt, Md.). 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (a workshop honoring Professor David A. Landgrebe): NASA Goddard Space Flight Visitor Center, Greenbelt, Maryland, USA, 27-28 October 2003. Piscataway, NJ: IEEE, 2004.

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J, Lavreau, and Bardinet Claude, eds. Image analysis, geological control, and radiometric survey of LAND[S]AT TM data in Tanzania. Tervuren, Belgique: Musee royal de l'Afrique centrale, 1988.

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Digital analysis of remotely sensed imagery. New York: McGraw-Hill, 2009.

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Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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2-D and 3-D image registration for medical, remote sensing, and industrial applications. Hoboken, NJ: J. Wiley & Sons, 2005.

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A hierarchical object-based approach for urban land-use classification from remote sensing data. Enschede, Netherlands: ITC, 2003.

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Book chapters on the topic "Remote Sensing Image Data Analysis"

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Richards, John A., and Xiuping Jia. "Data Fusion." In Remote Sensing Digital Image Analysis, 293–312. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_12.

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Richards, John A. "Multispectral Transformations of Image Data." In Remote Sensing Digital Image Analysis, 127–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-02462-1_6.

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Richards, John A. "Fourier Transformation of Image Data." In Remote Sensing Digital Image Analysis, 148–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-02462-1_7.

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Richards, John A. "Multispectral Transformations of Image Data." In Remote Sensing Digital Image Analysis, 133–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-88087-2_6.

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Richards, John A. "Fourier Transformation of Image Data." In Remote Sensing Digital Image Analysis, 155–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-88087-2_7.

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Richards, John A., and Xiuping Jia. "Interpretation of Hyperspectral Image Data." In Remote Sensing Digital Image Analysis, 313–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_13.

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Richards, John A., and Xiuping Jia. "Multispectral Transformations of Image Data." In Remote Sensing Digital Image Analysis, 133–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_6.

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Richards, John A., and Xiuping Jia. "Fourier Transformation of Image Data." In Remote Sensing Digital Image Analysis, 155–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_7.

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Richards, John A. "The Interpretation of Digital Image Data." In Remote Sensing Digital Image Analysis, 69–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-02462-1_3.

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Richards, John A. "The Interpretation of Digital Image Data." In Remote Sensing Digital Image Analysis, 75–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-88087-2_3.

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Conference papers on the topic "Remote Sensing Image Data Analysis"

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Teillet, P. M., Brian L. Markham, and Richard R. Irish. "Landsat radiometric cross-calibration: extended analysis of tandem image data sets." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2005. http://dx.doi.org/10.1117/12.626324.

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Liu, Zhigang, and Zhichao Sun. "Active one-class classification of remote sensing image." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.816115.

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Soergel, Uwe, and Ulrich Thoennessen. "Automatic geocoding of high-value targets using structural image analysis and GIS data." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373246.

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Shao, Zhenfeng, and Deren Li. "A strategy of using remote sensing image to update the geographical data." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.654553.

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Zhang, Hongsheng, and Yan Li. "Shape-adaptive neighborhood classification method for remote sensing image." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815857.

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Du, Qian. "Noise estimation for remote sensing image data analysis." In Optical Science and Technology, SPIE's 48th Annual Meeting, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2004. http://dx.doi.org/10.1117/12.508101.

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Liu, Liangming, and Deren Li. "Drought analysis based on remote sensing and ancillary data." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441382.

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He, Hui, and Xianchuan Yu. "A comparison of PCA/ICA for data preprocessing in remote sensing imagery classification." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.652298.

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Liu, Tingting, Pingxiang Li, Liangpei Zhang, and Xu Chen. "Multi-spectral remote sensing image retrieval based on semantic extraction." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815899.

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Jackson, Philip T. G., Carl J. Nelson, Jens Schiefele, and Boguslaw Obara. "Runway detection in High Resolution remote sensing data." In 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2015. http://dx.doi.org/10.1109/ispa.2015.7306053.

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Reports on the topic "Remote Sensing Image Data Analysis"

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Falconer, David G. L51774 Remote Sensing of Hazardous Ground Movement about Buried Gas Transmission Lines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1997. http://dx.doi.org/10.55274/r0011973.

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Reviews the available sensors for monitoring hazardous ground movement. Our review was limited to airborne and spaceborne sensors for access, performance, and productivity considerations. It was observed that certain ground movement is comparatively localized, e.g., earthquake faulting, while other activity may extend for thousands of kilometers, e.g., frost heave. Accordingly, we have considered two operating modes for the sensor-platform system, namely, site-by-site and continuous corridor. To determine the suitability of the candidate sensors for pipeline monitoring, we have assessed the expected performance, operational aspects, and cost of each sensor-platform combination as a function of operating mode. Finally, we have developed a business model for (1) operation of the recommended sensor systems by fee-for-service contractors; (2) analysis of the collected data by image-analysis specialists; and (3) use of the survey products by pipeline engineers.

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Kholoshyn, Ihor V., Olga V. Bondarenko, Olena V. Hanchuk, and Iryna M. Varfolomyeyeva. Cloud technologies as a tool of creating Earth Remote Sensing educational resources. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3885.

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This article is dedicated to the Earth Remote Sensing (ERS), which the authors believe is a great way to teach geography and allows forming an idea of the actual geographic features and phenomena. One of the major problems that now constrains the active introduction of remote sensing data in the educational process is the low availability of training aerospace pictures, which meet didactic requirements. The article analyzes the main sources of ERS as a basis for educational resources formation with aerospace images: paper, various individual sources (personal stations receiving satellite information, drones, balloons, kites and balls) and Internet sources (mainstream sites, sites of scientific-technical organizations and distributors, interactive Internet geoservices, cloud platforms of geospatial analysis). The authors point out that their geospatial analysis platforms (Google Earth Engine, Land Viewer, EOS Platform, etc.), due to their unique features, are the basis for the creation of information thematic databases of ERS. The article presents an example of such a database, covering more than 800 aerospace images and dynamic models, which are combined according to such didactic principles as high information load and clarity.

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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.

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Ducey, Craig. Hierarchical Image Analysis and Characterization of Scaling Effects in Remote Sensing. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.399.

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Lasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.

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Saltus, Allen, Maygarden Jr., Saucier Benjamin, and Roger T. Analysis and Technical Report of Remote Sensing Data for the USS Kinsman. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada375666.

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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.

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Carroll, Herbert B., and Genliang Guo. A New Methodology for Oil and Gas Exploration Using Remote Sensing Data and Surface Fracture Analysis. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/3244.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

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Scott R. Reeves and Randal L. Billingsley. Identifying Oil Exploration Leads using Intergrated Remote Sensing and Seismic Data Analysis, Lake Sakakawea, Fort Berthold Indian Reservation, Willistion Basin. Office of Scientific and Technical Information (OSTI), February 2004. http://dx.doi.org/10.2172/925463.

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