Written by more than 50 world leaders in the field, this book covers major topics in signal and image processing for remote sensing. The second edition features new chapters on compressive sensing, the super-resolution method in the mixed pixel problem with hyperspectral images, sparse representation for target detection and classification in hyperspectral imagery, SAR image processing from autofocusing to change detection, and a critical review of pansharpening. Additional topics new to this edition include non-negative matrix and tensor factorization, ISAR imaging of targets, and applications of the Huang-Hilbert transform. The text presents a unique signal-processing point of view for image processing.
Written by more than 50 world leaders in the field, this book covers major topics in signal and image processing for remote sensing. The second edition features new chapters on compressive sensing, the super-resolution method in the mixed pixel problem with hyperspectral images, sparse representation for target detection and classification in hyperspectral imagery, SAR image processing from autofocusing to change detection, and a critical review of pansharpening. Additional topics new to this edition include non-negative matrix and tensor factorization, ISAR imaging of targets, and applications of the Huang-Hilbert transform. The text presents a unique signal-processing point of view for image processing.
Signal Processing for Remote Sensing: On the Normalized Hilbert Transform and Its Applications to Remote Sensing. Nyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing Problems. Hydroacoustic Signal Classification Using Support Vector Machines. Huygens Construction and the Doppler Effect in Remote Detection. Compressed Remote Sensing. Context-Dependent Classification: An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions. NMF and NTF for Sea Ice SAR Feature Extraction and Classification. Relating Time-Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics. Use of a Prediction-Error Filter in Merging High- and Low-Resolution Images. Hyperspectral Microwave Atmospheric Sounding Using Neural Networks. Satellite Passive Millimeter-Wave Retrieval of Global Precipitation. Image Processing for Remote Sensing: On SAR Image Processing: From Focusing to Target Recognition. Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface. An ISAR Technique for Refocussing Moving Targets in SAR Images. Active Learning Methods in Classification of Remote Sensing Images. Crater Detection Based on Marked Point Processes. Probability Density Function Estimation for Classification of High-Resolution SAR Images. Random Forest Classification of Remote Sensing Data. Sparse Representation for Target Detection and Classification in Hyperspectral Imagery. Integration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined Resolution. Signal Subspace Identification in Hyperspecral Imagery. Image Classification and Object Detection Using Spatial Contextual Constraints. Data Fusion for Remote-Sensing Applications. Image Fusion in Remote Sensing with the Steered Hermite Transform. Wavelet-Based Multi/Hyperspectral Image Restoration and Fusion. The Land Cover Estimation with Satellite Image Using Neural Network. Twenty-Five Years of Pansharpening: A Critical Review and New Developments. Index.
C.H. Chen is Chancellor Professor of Electrical and Computer Engineering at the University of Massachusetts Dartmouth.
Praise for the First Edition ...this book will be useful to advance
automated image processing and the integration of remote sensor
data with ecosystem and atmospheric models. The unique idea of
combining signal processing with image processing is a good one and
is well timed with ongoing technological advancements.
—Ross Lunetta, co-editor of Remote Sensing Change Detection and
Remote Sensing and GIS Accuracy AssessmentOverall, the breadth and
depth of content make this book an excellent reference for
researchers, including graduate students, engaged in advanced
remote sensing data analysis, who will find that some chapters
provide inspiration to their own research.
—Qian Du, Department of Electrical and Computer Engineering,
Mississippi State University, in Photogrammetric Engineering &
Remote Sensing, Nov. 2007, Vol. 73, No. 11
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