Advances in signal and image processing for remote sensing have been tremendous in recent years. The progress has been particularly significant with the use of deep learning based techniques to solve remote sensing problems. These advancements are the focus of this third edition of Signal and Image Processing for Remote Sensing. It emphasizes the use of machine learning approaches for the extraction of remote sensing information. Other topics include change detection in remote sensing and compressed sensing. With 19 new chapters written by world leaders in the field, this book provides an authoritative examination and offers a unique point of view on signal and image processing.
Features
Includes all new content and does not replace the previous edition
Covers machine learning approaches in both signal and image processing for remote sensing
Studies deep learning methods for remote sensing information extraction that is found in other books
Explains SAR, microwave, seismic, GPR, and hyperspectral sensors and all sensors considered
Discusses improved pattern classification approaches and compressed sensing approaches
Provides ample examples of each aspect of both signal and image processing
This book is intended for university academics, researchers, postgraduate students, industry, and government professionals who use remote sensing and its applications.
