Signal and Image Processing for Remote Sensing

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience. This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing. The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing. New in This Edition The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include: Compressive sensing The mixed pixel problem with hyperspectral images Hyperspectral image (HSI) target detection and classification based on sparse representation An ISAR technique for refocusing moving targets in SAR images Empirical mode decomposition for signal processing Feature extraction for classification of remote sensing signals and images Active learning methods in classification of remote sensing images Signal subspace identification of hyperspectral data Wavelet-based multi/hyperspectral image restoration and fusion The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).

Signal Processing for Remote Sensing On the Normalized Hilbert Transform and Its Applications to Remote SensingSteven R. Long and Norden E. HuangNyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing ProblemsArnab Roy and John F. DohertyHydroacoustic Signal Classification Using Support Vector MachinesMatthias Tuma, Christian Igel, and Mark Prior Huygens Construction and the Doppler Effect in Remote DetectionEnders A. RobinsonCompressed Remote SensingJianwei Ma, A. Shaharyar Khwaja, and M. Yousuff HussainiContext-Dependent Classification: An Approach for Achieving Robust Remote Sensing Performance in Changing ConditionsChristopher R. Ratto, Kenneth D. Morton, Jr., Leslie M. Collins, and Peter A. TorrioneNMF and NTF for Sea Ice SAR Feature Extraction and ClassificationJuha KarvonenRelating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture DynamicsJan Verbesselt, P. Jönsson, S. Lhermitte, I. Jonckheere, J. van Aardt, and P.Coppin Use of a Prediction-Error Filter in Merging High- and Low-Resolution ImagesSong-Ho Yun and Howard Zebker Hyperspectral Microwave Atmospheric Sounding Using Neural NetworksWilliam J. BlackwellSatellite Passive Millimeter-Wave Retrieval of Global PrecipitationChinnawat "Pop" Surussavadee and David H. Staelin Image Processing for Remote SensingOn SAR Image Processing: From Focusing to Target RecognitionKun-Shan Chen and Yu-Chang TzengPolarimetric SAR Techniques for Remote Sensing of the Ocean SurfaceDale L. Schuler, Jong-Sen Lee, and Dayalan KasilingamAn ISAR Technique for Refocussing Moving Targets in SAR ImagesMarco Martorella, Elisa Giusti, Fabrizio Berizzi, Alessio Bacci, and Enzo Dalle MeseActive Learning Methods in Classification of Remote Sensing ImagesLorenzo Bruzzone, Claudio Persello, and Begüm DemirCrater Detection Based on Marked Point ProcessesGiulia Troglio, Jon Atli Benediktsson, Gabriele Moser, and Sebastiano Bruno SerpicoProbability Density Function Estimation for Classification of High-Resolution SAR ImagesVladimir A. Krylov, Gabriele Moser, Sebastiano Bruno Serpico, and Josiane ZerubiaRandom Forest Classification of Remote Sensing DataBjörn Waske, Jon Atli Benediktsson, and Johannes R. SveinssonSparse Representation for Target Detection and Classification in Hyperspectral ImageryYi Chen, Trac D. Tran, and Nasser M. NasrabdiIntegration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined ResolutionAlberto Villa, Jon Atli Benediktsson, Jocelyn Chanussot, and C. JuttenSignal Subspace Identification in Hyperspecral ImageryJosé M.P. Nascimento and José M. Bioucas-Dias Image Classification and Object Detection Using Spatial Contextual ConstraintsSelim Aksoy, R. Gökberk Cinbis, and H. Gökhan AkçayData Fusion for Remote-Sensing ApplicationsAnne H. S. SolbergImage Fusion in Remote Sensing with the Steered Hermite TransformBoris Escalante-Ramírez and Alejandra A. López-CalocaWavelet-Based Multi/Hyperspectral Image Restoration and FusionPaul Scheunders, Arno Duijster, and Yifan ZhangThe Land Cover Estimation with Satellite Image Using Neural NetworkYuta Tsuchida, Michifumi Yoshioka, Sigeru Omatu, and Toru FujinakaTwenty-Five Years of Pansharpening: A Critical Review and New DevelopmentsBruno Aiazzi, Luciano Alparone, Stefano Baronti, Andrea Garzelli, and Massimo Selva Index

Chi Hau Chen is currently the Chancellor Professor Emeritus of electrical and computer engineering at the University of Massachusetts Dartmouth, where he has taught since 1968. Dr. Chen has published 29 books in his areas of research. He served as associate editor of the IEEE Transactions on Acoustics, Speech and Signal Processing for four years, associate editor of the IEEE Transactions on Geoscience and Remote Sensing for 15 years, and since 2008 has been a board member of Pattern Recognition. Dr. Chen is a Life Fellow of the IEEE, a Fellow of the International Association of Pattern Recognition (IAPR), and a member of Academia NDT International. For more information about Dr. Chen, visit his web page at the University of Massachusetts Dartmouth.