An authoritative deep dive into the most recent machine learning approaches to hyperspectral remote sensing image processing In Machine Learning-Based Hyperspectral Image Processing, distinguished researcher Dr. Bing Zhang delivers an up-to-date discussion of machine learning-based approaches to hyperspectral image analysis. The author comprehensively reviews machine learning approaches to hyperspectral image denoising and super-resolution tasks, offering coverage of a variety of perspectives. Dr. Zhang also explores the most recent research on machine learning hyperspectral unmixing methods…mehr
An authoritative deep dive into the most recent machine learning approaches to hyperspectral remote sensing image processing In Machine Learning-Based Hyperspectral Image Processing, distinguished researcher Dr. Bing Zhang delivers an up-to-date discussion of machine learning-based approaches to hyperspectral image analysis. The author comprehensively reviews machine learning approaches to hyperspectral image denoising and super-resolution tasks, offering coverage of a variety of perspectives. Dr. Zhang also explores the most recent research on machine learning hyperspectral unmixing methods and hyperspectral image classification. He 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.
Bing Zhang, PhD, is Full Professor and Deputy Director of the Aerospace Information Research Institute, CAS. He has authored over 300 publications and currently serves as the Chief Editor for the Chinese Journal of Remote Sensing and Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing.
Inhaltsangabe
About the Editors List of Contributors Preface 1. Chapter1-Review for Machine Learning-Based Hyperspectral Image Analysis 1.1 Overview 1.2 Denoising 1.3 Super-resolution 1.4 Unmixing 1.5 Classification 1.6 Target Detection 1.7 Change Detection 1.8 Experimental Datasets 1.9 Chapter Arrangement and Writing Purpose 2. Chapter2-Hyperspectral Image Denoising Based on Low-Rank Regularization 2.1 Introduction 2.2 Model-driven Approaches 2.3 Data-driven Approaches 2.4 Conclusion and Outlook 3. Chapter3-Hyperspectral Image Denoising Based on Tensor Models 3.1 Introduction 3.2 HSI Construction 3.3 Tensor Modelling-based Reconstruction Methods 3.4 Numerical Experiments 3.5 Conclusion 4. Chapter4-Hyperspectral Image Denoising Based on Spatial-Spectral Joint Constraints 4.1 Non-Local Means Low-Rank Approximation 4.2 Wavelet-Based Block Low-Rank Representations 4.3 Conclusion 5. Chapter5-Hyperspectral Image Reconstruction Based on Spectral Super-resolution 5.1 Introduction 5.2 Experimental Datasets and Evaluation Indicators 5.3 A learning subpixel super-resolution model based on coupled dictionary 5.4 A collaborative spectral-super-resolution model based on adaptive learning 5.5 Conclusion 6. Chapter6-Hyperspectral Image Reconstruction Based on Supervised and Semi-Supervised Learning 6.1 Introduction 6.2 Full Supervised HSI SR 6.3 Weakly Supervised HSI SR 6.4 Self-Supervised HSI SR 6.5 Blind HSI SR 6.6 Conclusion and Discussion 7. Chapter7-Hyperspectral Image Reconstruction Based on Unsupervised Learning 7.1 Introduction 7.2 Problem Formulation 7.3 Unsupervised Hyperspectral Image Super-Resolution with Dirichlet-Net 7.4 Unsupervised and Unregistered Hyperspectral Image Super-Resolution 7.5 Improving SR Performance with Endmember Assisted Camera Response Function Learning 7.6 Conclusions 8. Chapter8-Hyperspectral Image Reconstruction Based on Adaptive Learning 8.1 Introduction 8.2 Problem Formulation 8.3 Numerical Model-Guided Nonlinear Spectral Unmixing 8.4 Experiment and Results 8.5 Conclusion 9. Chapter9-Hyperspectral Unmixing With Nonnegative Matrix Factorization 9.1 Introduction 9.2 Methodologies 9.3 Experiments 9.4 Conclusion 10. Chapter10-Hyperspectral Unmixing Based on Low-rank Representation and Sparse Constraints 10.1 Introduction 10.2 Linear Unmixing Algorithms 10.3 Hybrid Unmixing Algorithms 10.4 Experiments 10.5 Conclusions 11. Chapter11-Endmember purification and Endmember selection 11.1 Introduction 11.2 Endmember Purification 11.3 Unmixing with Geographic Knowledge Graph 11.4 Experimental Results and Analysis 11.5 Conclusion 12. Chapter12-Hyperspectral Unmixing Based on Deep Autoencoder Networks 12.1 Introduction 12.2 Methodologies 12.3 Experimental results 12.4 Conclusion 12.5 Discussion 13. Chapter13-Numerical Model-Guided Nonlinear Spectral Unmixing 13.1 Introduction 13.2 Nonlinear Mixture Models and Extensions 13.3 Numerical Model-Guided Nonlinear Spectral Unmixing 13.4 Conclusions 14. Chapter14-Spatial-Spectral Gabor-based Hyperspectral Image Classification 14.1 Spatial-Spectral Gabor Feature Extraction 14.2 Pixel-wise Gabor Features for Hyperspectral Image Classification 14.3 Superpixel-wise Gabor Features for HSI Classification 15. Chapter15-Domain Adaptation for Hyperspectral Image Classification 15.1 Basic Concepts of Domain Adaptation 15.2 Domain Adaptation for Hyperspectral Image Classification 15.3 Deep Domain Adaptation-based Hyperspectral Image Classification 15.4 Conclusion 16. Chapter16-Unsupervised Domain Adaptation for Classification of Hyperspectral Images 16.1 Introduction 16.2 Unsupervised Domain Adaptation Problem 16.3 Traditional Unsupervised Domain Adaptation Methods 16.4 Deep Learning Based Unsupervised Domain Adaptation Methods 16.5 Experimental Results and Analysis 17. Chapter17-Lightweight models for Hyperspectral Image Classification 17.1 Introduction 17.2 Lightweight Feature Extraction-based hyperspectral Image Classification 17.3 Experimental Results and Analysis 17.4 Conclusion 18. Chapter18-Ensemble Method Based Hyperspectral Image Classification 18.1 Background 18.2 Introduction to Ensemble Learning 18.3 Ensemble Learning in HSI Classification 18.4 Conclusion 19. Chapter19-Spectral-Spatial Hyperspectral Image Classification Based on Sparse Representation 19.1 Introduction 19.2 Related Models Description 19.3 Hyperspectral Image Classification Based on Sparse Representation 19.4 Experimental Results and Analysis 20. Chapter20-Hyperspectral Image Classification with Limited Samples 20.1 Introduction 20.2 Method 20.3 Experimental Results 21. Chapter21-Constrained Energy Minimization based Hyperspectral Image Target Detection 21.1 Introduction 21.2 Overview of CEM 21.3 CEM-based methods 21.4 Conclusions 22. Chapter22-Target Deteciton Using Multi-Domain Features in Hyperspectral Imagery 22.1 Introduction 22.2 Related Works 22.3 The Proposed Detection Methodology 22.4 Experiments and Analysis 22.5 Conclusion 23. Chapter23-Weakly Supervised Learning-based Hyperspectral Image Anomaly Target Detection 23.1 Introduction 23.2 Weakly Supervised Hyperspectral Anomaly Detection (WSLRR) 23.3 Weakly Supervised Hyperspectral Target Detection (BLTSC) 23.4 Rank-Aware Hyperspectral Band Selection (R-GAN) 23.5 Conclusions 24. Chapter24-Hyperspectral anomaly detection via background-separable model 24.1 Hyperspectral Anomaly Detection Using Dual Window Density 24.2 Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis 24.3 Ensemble Entropy Metric for Hyperspectral Anomaly Detection 25. Chapter25-Spectral Change Analysis for Multitemporal Change Detection in Hyperspectral Remote Sensing Images 25.1 Introduction 25.2 Related works 25.3 Spectral Change Analysis in Hyperspectral images 25.4 Experimental setup 25.5 Results and Analysis 25.6 Conclusion 26. Chapter26-Challenges and Future Directions 26.1 Challenges and Future Directions in Hyperspectral Image Denoising 26.2 Challenges and Future Directions in Hyperspectral (HS) and Multispectral (MS) Image Fusion 26.3 Challenges and Future Directions in NMF-Based Hyperspectral Unmixing 26.4 Challenges and Future Directions in Knowledge Graph-Enhanced Hyperspectral Unmixing 26.5 Challenges and Future Directions in Numerical Model-Guided Nonlinear Hyperspectral Unmixing 26.6 Challenges and Future Directions in Hyperspectral Image Classification 26.7 Chapter on Challenges and Future Directions in Hyperspectral Target Detection Index
