Machine-Learning-Based Hyperspectral Image Processing

Bing Zhang

• Publisher: Wiley-IEEE Press
• Year: 2026
• Print ISBN:
  9781394267859
• Edition: 1
• eBook ISBN:
  9781394267866
• Topic: Technology & Engineering
• Subtopic: Digital Media
• Index: Technology & Engineering

An authoritative deep dive into the most recent machine learning approaches to hyperspectral remote sensing image processing

In Machine-Learning-Based Hyperspectral Image Processing, a team of distinguished researchers led by Dr. Bing Zhang delivers an up-to-date discussion of machine learning-based approaches to hyperspectral image analysis. The contributors comprehensively review machine learning approaches to hyperspectral image denoising and super-resolution tasks, offering coverage of a variety of perspectives.

The book also explores the most recent research on machine learning hyperspectral unmixing methods and hyperspectral image classification. It explains the algorithms used for hyperspectral image target and change detection, as well.

Readers will also find:

• A thorough introduction to the novel concept of applying advanced machine learning techniques to the analysis of hyperspectral imagery
• Comprehensive explorations of the most recent developments in this technology and its applications
• Practical discussions of how to effectively process and extract valuable insights from hyperspectral data
• Complete treatments of a variety of hyperspectral remote sensing image processing tasks, including classification, target detection, and change detection.

Perfect for postgraduate students and research scientists with an interest in the subject, Machine-Learning-Based Hyperspectral Image Processing will also benefit researchers, academicians, and students engaged in machine learning-based approaches to image analysis.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• About the Editor
• List of Contributors
• 1 Review for Machine‐Learning‐Based Hyperspectral Image Analysis
• 2 Hyperspectral Image Denoising Based on Low‐rank Regularization
• 3 Hyperspectral Image Denoising Based on Tensor Models
• 4 Hyperspectral Image Denoising Based on Spatial–Spectral Joint Constraints
• 5 Hyperspectral Image Reconstruction Based on Spectral Super‐resolution
• 6 Hyperspectral Image Reconstruction From Supervision to Blindness
• 7 Hyperspectral Image Reconstruction Based on Unsupervised Learning
• 8 Hyperspectral Image Reconstruction Based on Adaptive Learning
• 9 Hyperspectral Unmixing with Nonnegative Matrix Factorization
• 10 Hyperspectral Unmixing Based on Low‐rank Representation and Sparse Constraint
• 11 Endmember Purification and Geographical Knowledge Graph‐guided Endmember Selection
• 12 Hyperspectral Unmixing Based on Deep Autoencoder Networks
• 13 Numerical‐model‐guided Nonlinear Spectral Unmixing
• 14 Spatial–Spectral Gabor‐based Hyperspectral Image Classification
• 15 Domain Adaptation for Hyperspectral Image Classification
• 16 Unsupervised Domain Adaptation for Classification of Hyperspectral Images
• 17 Lightweight Models for Hyperspectral Image Classification
• 18 Ensemble Method Based Hyperspectral Image Classification
• 19 Spectral‐Spatial Hyperspectral Image Classification Based on Sparse Representation
• 20 Hyperspectral Image Classification with Limited Samples
• 21 Constrained Energy Minimization Based Hyperspectral Image Target Detection
• 22 Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation
• 23 Weakly Supervised Learning‐based Hyperspectral Image Anomaly/Target Detection
• 24 Hyperspectral Anomaly Detection via Background‐separable Mode
• 25 Spectral Change Analysis for Multitemporal Change Detection in Hyperspectral Remote Sensing Images
• 26 Challenges and Future Directions
• Index
• End User License Agreement