Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgements
Editor
List of Contributors
Part I: General Topics
Chapter 1 A Brief Overview of 60 Years of Progress on Signal/Image Processing for Remote Sensing
Chapter 2 Proven Approaches of Using Innovative High-Performance Computing Architectures in Remote Sensing
Part II: Signal Processing for Remote Sensing
Chapter 3 Machine Learning Techniques for Geophysical Parameter Retrievals
Chapter 4 Subsurface Inverse Profiling and Imaging Using Stochastic Optimization Techniques
Chapter 5 Close and Remote Ground Penetrating Radar Surveys via Microwave Tomography: State of Art and Perspectives
Chapter 6 Polarimetric SAR Signature of Complex Scene: A Simulation Study
Chapter 7 Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data
Chapter 8 Riemannian Clustering of PolSAR Data Using the Polar Decomposition
Chapter 9 Seismic Velocity Picking Using Hopfield Neural Network
Chapter 10 Expanded Radial Basis Function Network with Proof of Hidden Node Number by Recurrence Relation for Well Log Data Inversion
Part III: Image Processing for Remote Sensing
Chapter 11 Convolutional Neural Networks Meet Markov Random Fields for Semantic Segmentation of Remote Sensing Images
Chapter 12 Deep Learning Methods for Satellite Image Super-Resolution
Chapter 13 Machine Learning in Remote Sensing
Chapter 14 Robust Training of Deep Neural Networks with Weakly Labelled Data
Chapter 15 Semantic Segmentation with OTBTF and Keras
Chapter 16 Performance of a Diffusion Model for Instance Segmentation in Remote Sensing Imagery
Chapter 17 Land Cover Classification Using Attention-Based Multi-Modal Image Fusion: An Explainable Analysis
Chapter 18 FPGA Compressive Sensing Method Applied to Hyperspectral Imagery
Chapter 19 Large-Scale Fine-Grained Change Detection from Multisensory Satellite Images
Chapter 20 Change Detection on Graphs: Exploiting Graph Structure from Bi-temporal Satellite Imagery
Chapter 21 Target Detection in Hyperspectral Imaging Using Neural Networks
Index