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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 hy
List of contents
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 Pansha
About the author
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.
Summary
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 hy
