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.
PART I General Topics
Chapter 1 A Brief Overview of 60 Years of Progress on Signal/Image Processing
for
Remote Sensing
C.H. Chen
Chapter 2 Proven Approaches of Using Innovative HighPerformance Computing
Architectures in Remote Sensing
Rocco Sedona, Gabriele Cavallaro, Morris Riedel, and Jon Atli Benediktsson
PART II Signal Processing for Remote Sensing
Chapter 3 Machine Learning Techniques for Geophysical Parameter Retrievals
Adam B. Milstein, Michael Pieper, and William J. Blackwell
Chapter 4 Subsurface Inverse Profiling and Imaging Using Stochastic
Optimization Techniques
Maryam Hajebi and Ahmad Hoorfar
Chapter 5 Close and Remote Ground Penetrating Radar Surveys via Microwave
Tomography: State of Art and Perspectives
Gianluca Gennarelli, Giuseppe Esposito, Giovanni Ludeno,
Francesco Soldovieri, and Ilaria Catapano
Chapter 6 Polarimetric SAR Signature of Complex Scene: A Simulation Study
KunShan Chen, ChengYen Chiang, and Ying Yang
Chapter 7 Machine Learning for Arctic Sea Ice Physical Properties Estimation
Using DualPolarimetric SAR Data
Katalin Blix, Martine M. Espeseth, and Torbjorn Eltoft
Chapter 8 Riemannian Clustering of PolSAR Data Using the Polar Decomposition
Madalina Ciuca, Gabriel Vasile, Marco Congedo, and Michel Gay
Chapter 9 Seismic Velocity Picking Using Hopfield Neural Network
KouYuan Huang and JiaRong Yang
Chapter 10 Expanded Radial Basis Function Network with Proof of Hidden Node
Number by Recurrence Relation for Well Log Data Inversion
KouYuan Huang, LiangChi Shen, JiunDer You, and LiSheng Weng
PART III Image Processing for Remote Sensing
Chapter 11 Convolutional Neural Networks Meet Markov Random Fields for
Semantic Segmentation of Remote Sensing Images
Martina Pastorino, Gabriele Moser, Sebastiano B. Serpico, and Josiane
Zerubia
Chapter 12 Deep Learning Methods for Satellite Image SuperResolution
Diego Valsesia and Enrico Magli
Chapter 13 Machine Learning in Remote Sensing
Ronny Hansch
Chapter 14 Robust Training of Deep Neural Networks with Weakly Labelled Data
Gianmarco Perantoni and Lorenzo Bruzzone
Chapter 15 Semantic Segmentation with OTBTF and Keras
Remi Cresson
Chapter 16 Performance of a Diffusion Model for Instance Segmentation in
Remote Sensing Imagery
Selin Koles, Sedat Ozer, and C.H. Chen
Chapter 17 Land Cover Classification Using AttentionBased MultiModal Image
Fusion: An Explainable Analysis
Oktay Karakus, Wanli Ma, and Paul L. Rosin
Chapter 18 FPGA Compressive Sensing Method Applied to Hyperspectral
ImageryJose Nascimento and Mario Vestias
Chapter 19 LargeScale FineGrained Change Detection from Multisensory
Satellite Images
Andrea Garzelli and Claudia Zoppetti
Chapter 20 Change Detection on Graphs: Exploiting Graph Structure from
Bitemporal Satellite Imagery
Juan F. FlorezOspina, Hernan D. BenitezRestrepo, and David A.
JimenezSierra
Chapter 21 Target Detection in Hyperspectral Imaging Using Neural Networks
Edisanter Lo and Emmett Ientilucci
Prof. C.H. Chen received his Ph. D in electrical engineering from Purdue University West Lafayette, Indiana, in 1965, his MSEE from the University of Tennessee, Knoxville, in 1962, and his BSEE from the National Taiwan University, Taipei in 1959. He is currently the chancellor professor emeritus of electrical and computer engineering at the University of Massachusetts Dartmouth, where he has been a faculty member since 1968. His research areas encompass statistical pattern recognition and signal/image processing with applications to remote sensing, medical imaging, geophysical, underwater acoustics, and nondestructive testing problems, as well as computer vision for video surveillance, time-series analysis, and neural networks. He has edited and authored 37 books in his areas of research, including Digital Waveform Processing and Recognition (CRC Press 1982), Signal and Image Processing for Remote Sensing (CRC Press, first edition 2006, second edition 2012), and Compressive Sensing of Earth Observations (CRC Press 2017). He served as associate editor of the IEEE Transactions on Acoustic, Speech, and Signal Processing for 4 years, associate editor of the IEEE Transactions on Geoscience and Remote Sensing for 15 years, and since 2008 he has been a board member/associate editor of Pattern Recognition particularly on remote sensing topics. Dr. Chen has been a Fellow of the Institute of Electrical and Electronic Engineers (IEEE) since 1988, a Life Fellow of the IEEE since 2003, and a Fellow of the International Association of Pattern Recognition (IAPR) since 1996. He is also the editor of the book series entitled Signal and Image Processing of Earth Observations, for CRC Press.
