Signal and Image Processing for Remote Sensing

by C.H. Chen (Editor)

Overview

This new edition of Signal and Image Processing for Remote Sensing emphasizes the use of machine learning approaches to remote sensing information extraction, change detection, and compressed sensing. With 19 new chapters written by world leaders in the field, it provides an authoritative coverage of the recent progress in the field.

Product Details

ISBN-13:	9781032437415
Publisher:	CRC Press
Publication date:	06/11/2024
Series:	Signal and Image Processing of Earth Observations
Edition description:	3rd ed.
Pages:	432
Product dimensions:	7.00(w) x 10.00(h) x (d)

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.

Table 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

What People are Saying About This

From the Publisher

"...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, Remote Sensing Change Detection and Remote Sensing and GIS Accuracy Assessment

"Overall, 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

Editorial Reviews

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 Assessment

Overall, 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

From the Publisher