Machine Learning-Based Hyperspectral Image Processing

Nyhet

Inbunden, Engelska, 2026

AvBing Zhang,China) Zhang, Bing (Chinese Academy of Sciences

2 029 kr

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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 imageryComprehensive explorations of the most recent developments in this technology and its applicationsPractical discussions of how to effectively process and extract valuable insights from hyperspectral dataComplete 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.

Produktinformation

Utgivningsdatum2026-04-14
FormatInbunden
SpråkEngelska
Antal sidor704
FörlagJohn Wiley & Sons Inc
ISBN9781394267859

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT

About the EditorsList of ContributorsPreface1. Chapter1-Review for Machine Learning-Based Hyperspectral Image Analysis1.1 Overview1.2 Denoising1.3 Super-resolution1.4 Unmixing1.5 Classification1.6 Target Detection1.7 Change Detection1.8 Experimental Datasets1.9 Chapter Arrangement and Writing Purpose2. Chapter2-Hyperspectral Image Denoising Based on Low-Rank Regularization2.1 Introduction2.2 Model-driven Approaches2.3 Data-driven Approaches2.4 Conclusion and Outlook3. Chapter3-Hyperspectral Image Denoising Based on Tensor Models3.1 Introduction3.2 HSI Construction3.3 Tensor Modelling-based Reconstruction Methods3.4 Numerical Experiments3.5 Conclusion4. Chapter4-Hyperspectral Image Denoising Based on Spatial-Spectral Joint Constraints4.1 Non-Local Means Low-Rank Approximation4.2 Wavelet-Based Block Low-Rank Representations4.3 Conclusion5. Chapter5-Hyperspectral Image Reconstruction Based on Spectral Super-resolution5.1 Introduction5.2 Experimental Datasets and Evaluation Indicators5.3 A learning subpixel super-resolution model based on coupled dictionary5.4 A collaborative spectral-super-resolution model based on adaptive learning5.5 Conclusion6. Chapter6-Hyperspectral Image Reconstruction Based on Supervised and Semi-Supervised Learning6.1 Introduction6.2 Full Supervised HSI SR6.3 Weakly Supervised HSI SR6.4 Self-Supervised HSI SR6.5 Blind HSI SR6.6 Conclusion and Discussion7. Chapter7-Hyperspectral Image Reconstruction Based on Unsupervised Learning7.1 Introduction7.2 Problem Formulation7.3 Unsupervised Hyperspectral Image Super-Resolution with Dirichlet-Net7.4 Unsupervised and Unregistered Hyperspectral Image Super-Resolution7.5 Improving SR Performance with Endmember Assisted Camera Response Function Learning7.6 Conclusions8. Chapter8-Hyperspectral Image Reconstruction Based on Adaptive Learning8.1 Introduction8.2 Problem Formulation8.3 Numerical Model-Guided Nonlinear Spectral Unmixing8.4 Experiment and Results8.5 Conclusion9. Chapter9-Hyperspectral Unmixing With Nonnegative Matrix Factorization9.1 Introduction9.2 Methodologies9.3 Experiments9.4 Conclusion10. Chapter10-Hyperspectral Unmixing Based on Low-rank Representation and Sparse Constraints10.1 Introduction10.2 Linear Unmixing Algorithms10.3 Hybrid Unmixing Algorithms10.4 Experiments10.5 Conclusions11. Chapter11-Endmember purification and Endmember selection11.1 Introduction11.2 Endmember Purification11.3 Unmixing with Geographic Knowledge Graph11.4 Experimental Results and Analysis11.5 Conclusion12. Chapter12-Hyperspectral Unmixing Based on Deep Autoencoder Networks12.1 Introduction12.2 Methodologies12.3 Experimental results12.4 Conclusion12.5 Discussion13. Chapter13-Numerical Model-Guided Nonlinear Spectral Unmixing13.1 Introduction13.2 Nonlinear Mixture Models and Extensions13.3 Numerical Model-Guided Nonlinear Spectral Unmixing13.4 Conclusions14. Chapter14-Spatial-Spectral Gabor-based Hyperspectral Image Classification14.1 Spatial-Spectral Gabor Feature Extraction14.2 Pixel-wise Gabor Features for Hyperspectral Image Classification14.3 Superpixel-wise Gabor Features for HSI Classification15. Chapter15-Domain Adaptation for Hyperspectral Image Classification15.1 Basic Concepts of Domain Adaptation15.2 Domain Adaptation for Hyperspectral Image Classification15.3 Deep Domain Adaptation-based Hyperspectral Image Classification15.4 Conclusion16. Chapter16-Unsupervised Domain Adaptation for Classification of Hyperspectral Images16.1 Introduction16.2 Unsupervised Domain Adaptation Problem16.3 Traditional Unsupervised Domain Adaptation Methods16.4 Deep Learning Based Unsupervised Domain Adaptation Methods16.5 Experimental Results and Analysis17. Chapter17-Lightweight models for Hyperspectral Image Classification17.1 Introduction17.2 Lightweight Feature Extraction-based hyperspectral Image Classification17.3 Experimental Results and Analysis17.4 Conclusion18. Chapter18-Ensemble Method Based Hyperspectral Image Classification18.1 Background18.2 Introduction to Ensemble Learning18.3 Ensemble Learning in HSI Classification18.4 Conclusion19. Chapter19-Spectral-Spatial Hyperspectral Image Classification Based on Sparse Representation19.1 Introduction19.2 Related Models Description19.3 Hyperspectral Image Classification Based on Sparse Representation19.4 Experimental Results and Analysis20. Chapter20-Hyperspectral Image Classification with Limited Samples20.1 Introduction20.2 Method20.3 Experimental Results21. Chapter21-Constrained Energy Minimization based Hyperspectral Image Target Detection21.1 Introduction21.2 Overview of CEM21.3 CEM-based methods21.4 Conclusions22. Chapter22-Target Deteciton Using Multi-Domain Features in Hyperspectral Imagery22.1 Introduction22.2 Related Works22.3 The Proposed Detection Methodology22.4 Experiments and Analysis22.5 Conclusion23. Chapter23-Weakly Supervised Learning-based Hyperspectral Image Anomaly Target Detection23.1 Introduction23.2 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 Conclusions24. Chapter24-Hyperspectral anomaly detection via background-separable model24.1 Hyperspectral Anomaly Detection Using Dual Window Density24.2 Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis24.3 Ensemble Entropy Metric for Hyperspectral Anomaly Detection25. Chapter25-Spectral Change Analysis for Multitemporal Change Detection in Hyperspectral Remote Sensing Images25.1 Introduction25.2 Related works25.3 Spectral Change Analysis in Hyperspectral images25.4 Experimental setup25.5 Results and Analysis25.6 Conclusion26. Chapter26-Challenges and Future Directions26.1 Challenges and Future Directions in Hyperspectral Image Denoising26.2 Challenges and Future Directions in Hyperspectral (HS) and Multispectral (MS) Image Fusion26.3 Challenges and Future Directions in NMF-Based Hyperspectral Unmixing26.4 Challenges and Future Directions in Knowledge Graph-Enhanced Hyperspectral Unmixing26.5 Challenges and Future Directions in Numerical Model-Guided Nonlinear Hyperspectral Unmixing26.6 Challenges and Future Directions in Hyperspectral Image Classification26.7 Chapter on Challenges and Future Directions in Hyperspectral Target DetectionIndex