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.

• About the Editor xxiList of Contributors xxiii1 Review for Machine-Learning-Based Hyperspectral Image Analysis 1He Sun and Ruitong Du1.1 Overview 11.2 Denoising 21.3 Super-resolution 41.4 Unmixing 71.5 Classification 81.6 Target Detection 111.7 Change Detection 131.8 Experimental Datasets 151.9 Chapter Arrangement and Writing Purpose 192 Hyperspectral Image Denoising Based on Low-rank Regularization 31Yong Chen, Hongyu Chen, and Wei He2.1 Introduction 312.2 Model-driven Approaches 322.3 Data-driven Approaches 412.4 Conclusion and Outlook 463 Hyperspectral Image Denoising Based on Tensor Models 51Yu-Bang Zheng, Jian-Li Wang, and Xi-Le Zhao3.1 Introduction 513.2 HSI Reconstruction 523.3 Tensor Modeling-based HSI Reconstruction Methods 533.4 Numerical Experiments 663.5 Conclusion 674 Hyperspectral Image Denoising Based on Spatial–Spectral Joint Constraints 73Bin Zhao, Magnus O. Ulfarsson, Jakob Sigurdsson, Jon Atli Benediktsson, and Jocelyn Chanussot4.1 Non-local Means Low-rank Approximation 734.2 Wavelet-based Block Low-rank Representations 794.3 Conclusions 845 Hyperspectral Image Reconstruction Based on Spectral Super-resolution 87Prof. Yanfeng Gu5.1 Introduction 875.2 Experimental Datasets and Evaluation Indicators 905.3 A Learning Subpixel Super-resolution Model Based on Coupled Dictionary 955.4 A Collaborative Spectral-super-resolution Model Based on Adaptive Learning 1065.5 Conclusion 1246 Hyperspectral Image Reconstruction From Supervision to Blindness 129Jie Xie, Jie Wu, Zhicheng Wang, Lina Zhuang, and Leyuan Fang6.1 Introduction 1296.2 Full Supervised HSI SR 1326.3 Weakly Supervised HSI SR 1376.4 Self-supervised HSI SR 1536.5 Blind HSI SR 1696.6 Conclusion and Discussion 1817 Hyperspectral Image Reconstruction Based on Unsupervised Learning 191Ying Qu, Jiangsan Zhao, Hairong Qi, Chiman Kwan, and Liqiang Zhang7.1 Introduction 1917.2 Problem Formulation 1937.3 Unsupervised Hyperspectral Image Super-resolution with Dirichlet Net 1937.4 Unsupervised and Unregistered Hyperspectral Image Super-resolution 1967.5 Improving SR Performance with Endmember-assisted Camera Response Function Learning 2007.6 Conclusions 2018 Hyperspectral Image Reconstruction Based on Adaptive Learning 207Ke Zheng, Jiaxin Li, Lianru Gao, and Bing Zhang8.1 Introduction 2078.2 Problem Formulation 2088.3 Numerical Model-guided Nonlinear Spectral Unmixing 2098.4 Experiment and Results 2188.5 Conclusion 2279 Hyperspectral Unmixing with Nonnegative Matrix Factorization 229Jun Li, Yuanchao Su, Shaoquan Zhang, and Ruoqing Xu9.1 Introduction 2299.2 Methodologies 2309.3 Experiments 2359.4 Conclusion 23910 Hyperspectral Unmixing Based on Low-rank Representation and Sparse Constraint 243Xiangrong Zhang, Jingyan Zhang, Guanchun Wang, and Licheng Jiao10.1 Introduction 24310.2 Linear Unmixing Algorithms 24410.3 Hybrid Unmixing Algorithms 25110.4 Experiments 25710.5 Conclusions 26611 Endmember Purification and Geographical Knowledge Graph-guided Endmember Selection 271Wenfei Luo and Rui Wu11.1 Introduction 27111.2 Endmember Purification 27211.3 Unmixing with Geographic Knowledge Graph 28311.4 Experimental Results and Analysis 28911.5 Conclusion 29812 Hyperspectral Unmixing Based on Deep Autoencoder Networks 301Yuanchao Su, Jun Li, Lianru Gao, Ruoqing Xu, Zhiqing Zhu, and Paolo Gamba12.1 Introduction 30112.2 Methodologies 30212.3 Experimental Results 31412.4 Conclusion 31812.5 Discussion 31813 Numerical-model-guided Nonlinear Spectral Unmixing 321Bin Yang and Bin Wang13.1 Introduction 32113.2 Nonlinear Mixture Models and Extensions 32413.3 Numerical-model-guided Nonlinear Spectral Unmixing 32713.4 Conclusions 34413.5 Challenges and Future Directions 34614 Spatial–Spectral Gabor-based Hyperspectral Image Classification 351Sen Jia, Shuyu Zhang, Qi Ren, Wangquan He, Meng Xu, and Jiasong Zhu14.1 Spatial–Spectral Gabor Feature Extraction 35114.2 Pixel-wise Gabor Features for Hyperspectral Image Classification 35914.3 Superpixel-wise Gabor Features for HSI Classification 37215 Domain Adaptation for Hyperspectral Image Classification 389Chong Li, Weiwei Sun, Jiangtao Peng, and Kai Ren15.1 Basic Concepts of Domain Adaptation 38915.2 Domain Adaptation for Hyperspectral Image Classification 39015.3 Deep Domain-adaptation-based Hyperspectral Image Classification 39115.4 Conclusion 40216 Unsupervised Domain Adaptation for Classification of Hyperspectral Images 405li Ma and Qian Du16.1 Introduction 40516.2 Unsupervised Domain Adaptation Problem 40816.3 Traditional Unsupervised Domain Adaptation Methods 40816.4 Deep-learning-based Unsupervised Domain Adaptation Methods 41116.5 Experimental Results and Analysis 41416.6 Conclusions 42017 Lightweight Models for Hyperspectral Image Classification 425Hongmin Gao, Shufang Xu, Zhonghao Chen, and Yiyan Zhang17.1 Introduction 42517.2 Lightweight Feature Extraction-based Hyperspectral Image Classification 42717.3 Experimental Results and Analysis 43817.4 Conclusion 44618 Ensemble Method Based Hyperspectral Image Classification 453Wei Feng and Mengdao Xing18.1 Background 45318.2 Introduction to Ensemble Learning 45418.3 Ensemble Learning in HSI Classification 45918.4 Conclusion 46719 Spectral-Spatial Hyperspectral Image Classification Based on Sparse Representation 471Haoyang Yu, Jia Jia, Chuhan Shen, Jiaochan Hu, Chein-I Chang, and Lianru Gao19.1 Introduction 47119.2 Related Models Description 47219.3 Hyperspectral Image Classification Based on Sparse Representation 47519.4 Experimental Results and Analysis 48519.5 Conclusion 49520 Hyperspectral Image Classification with Limited Samples 499Yuebin Wang, Liqiang Zhang, Bing Zhang, Antonio Plaza, and Xiao Xiang Zhu20.1 Introduction 49920.2 Method 50320.3 Experimental Results 50920.4 Conclusions 51921 Constrained Energy Minimization Based Hyperspectral Image Target Detection 521Zhenwei Shi, Zhengxia Zou, Bowen Chen, and Liqin Liu21.1 Introduction 52121.2 Overview of CEM 52221.3 CEM-based Methods 52521.4 Conclusions 53922 Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation 543Wei Li and Kun Gao22.1 Introduction 54322.2 Related Works 54522.3 The Proposed Detection Methodology 54622.4 Experiments and Analysis 55122.5 Conclusion 55823 Weakly Supervised Learning-based Hyperspectral Image Anomaly/Target Detection 561Weiying Xie, Xin Zhang, Yunsong Li, and Qian Du23.1 Introduction 56123.2 Weakly Supervised Hyperspectral Anomaly Detection (WSLRR) 56423.3 Weakly Supervised Hyperspectral Target Detection (BLTSC) 57323.4 Rank-aware Hyperspectral Band Selection (R-GAN) 57823.5 Conclusions 58724 Hyperspectral Anomaly Detection via Background-separable Mode 593Bing Tu, Xianchang Yang, Jun Li, Antonio Plaza, and Kaiyuan Chen24.1 Hyperspectral Anomaly Detection Using Dual Window Density 59324.2 Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis 60324.3 Ensemble Entropy Metric for Hyperspectral Anomaly Detection 61925 Spectral Change Analysis for Multitemporal Change Detection in Hyperspectral Remote Sensing Images 633Sicong Liu, Kecheng Du, Xiaohua Tong, and Peijun Du25.1 Introduction 63325.2 Related Works 63525.3 Spectral Change Analysis in Hyperspectral Images 63725.4 Experimental Setup 64325.5 Results and Analysis 64325.6 Conclusion 650Acknowledgement 65026 Challenges and Future Directions 655Bing Zhang, He Sun, and Ruitong Du26.1 Challenges and Future Directions in Hyperspectral Image Denoising 65526.2 Challenges and Future Directions in Hyperspectral (HS) and Multispectral (MS) Image Fusion 65726.3 Challenges and Future Directions in NMF-based Hyperspectral Unmixing 65926.4 Challenges and Future Directions in Knowledge Graph-enhanced Hyperspectral Unmixing 66026.5 Challenges and Future Directions in Numerical Model-guided Nonlinear Hyperspectral Unmixing 66026.6 Challenges and Future Directions in Hyperspectral Image Classification 66126.7 Chapter on Challenges and Future Directions in Hyperspectral Target Detection 662References 663Index 665

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.

• About the Editor xxiList of Contributors xxiii1 Review for Machine-Learning-Based Hyperspectral Image Analysis 1He Sun and Ruitong Du1.1 Overview 11.2 Denoising 21.3 Super-resolution 41.4 Unmixing 71.5 Classification 81.6 Target Detection 111.7 Change Detection 131.8 Experimental Datasets 151.9 Chapter Arrangement and Writing Purpose 192 Hyperspectral Image Denoising Based on Low-rank Regularization 31Yong Chen, Hongyu Chen, and Wei He2.1 Introduction 312.2 Model-driven Approaches 322.3 Data-driven Approaches 412.4 Conclusion and Outlook 463 Hyperspectral Image Denoising Based on Tensor Models 51Yu-Bang Zheng, Jian-Li Wang, and Xi-Le Zhao3.1 Introduction 513.2 HSI Reconstruction 523.3 Tensor Modeling-based HSI Reconstruction Methods 533.4 Numerical Experiments 663.5 Conclusion 674 Hyperspectral Image Denoising Based on Spatial–Spectral Joint Constraints 73Bin Zhao, Magnus O. Ulfarsson, Jakob Sigurdsson, Jon Atli Benediktsson, and Jocelyn Chanussot4.1 Non-local Means Low-rank Approximation 734.2 Wavelet-based Block Low-rank Representations 794.3 Conclusions 845 Hyperspectral Image Reconstruction Based on Spectral Super-resolution 87Prof. Yanfeng Gu5.1 Introduction 875.2 Experimental Datasets and Evaluation Indicators 905.3 A Learning Subpixel Super-resolution Model Based on Coupled Dictionary 955.4 A Collaborative Spectral-super-resolution Model Based on Adaptive Learning 1065.5 Conclusion 1246 Hyperspectral Image Reconstruction From Supervision to Blindness 129Jie Xie, Jie Wu, Zhicheng Wang, Lina Zhuang, and Leyuan Fang6.1 Introduction 1296.2 Full Supervised HSI SR 1326.3 Weakly Supervised HSI SR 1376.4 Self-supervised HSI SR 1536.5 Blind HSI SR 1696.6 Conclusion and Discussion 1817 Hyperspectral Image Reconstruction Based on Unsupervised Learning 191Ying Qu, Jiangsan Zhao, Hairong Qi, Chiman Kwan, and Liqiang Zhang7.1 Introduction 1917.2 Problem Formulation 1937.3 Unsupervised Hyperspectral Image Super-resolution with Dirichlet Net 1937.4 Unsupervised and Unregistered Hyperspectral Image Super-resolution 1967.5 Improving SR Performance with Endmember-assisted Camera Response Function Learning 2007.6 Conclusions 2018 Hyperspectral Image Reconstruction Based on Adaptive Learning 207Ke Zheng, Jiaxin Li, Lianru Gao, and Bing Zhang8.1 Introduction 2078.2 Problem Formulation 2088.3 Numerical Model-guided Nonlinear Spectral Unmixing 2098.4 Experiment and Results 2188.5 Conclusion 2279 Hyperspectral Unmixing with Nonnegative Matrix Factorization 229Jun Li, Yuanchao Su, Shaoquan Zhang, and Ruoqing Xu9.1 Introduction 2299.2 Methodologies 2309.3 Experiments 2359.4 Conclusion 23910 Hyperspectral Unmixing Based on Low-rank Representation and Sparse Constraint 243Xiangrong Zhang, Jingyan Zhang, Guanchun Wang, and Licheng Jiao10.1 Introduction 24310.2 Linear Unmixing Algorithms 24410.3 Hybrid Unmixing Algorithms 25110.4 Experiments 25710.5 Conclusions 26611 Endmember Purification and Geographical Knowledge Graph-guided Endmember Selection 271Wenfei Luo and Rui Wu11.1 Introduction 27111.2 Endmember Purification 27211.3 Unmixing with Geographic Knowledge Graph 28311.4 Experimental Results and Analysis 28911.5 Conclusion 29812 Hyperspectral Unmixing Based on Deep Autoencoder Networks 301Yuanchao Su, Jun Li, Lianru Gao, Ruoqing Xu, Zhiqing Zhu, and Paolo Gamba12.1 Introduction 30112.2 Methodologies 30212.3 Experimental Results 31412.4 Conclusion 31812.5 Discussion 31813 Numerical-model-guided Nonlinear Spectral Unmixing 321Bin Yang and Bin Wang13.1 Introduction 32113.2 Nonlinear Mixture Models and Extensions 32413.3 Numerical-model-guided Nonlinear Spectral Unmixing 32713.4 Conclusions 34413.5 Challenges and Future Directions 34614 Spatial–Spectral Gabor-based Hyperspectral Image Classification 351Sen Jia, Shuyu Zhang, Qi Ren, Wangquan He, Meng Xu, and Jiasong Zhu14.1 Spatial–Spectral Gabor Feature Extraction 35114.2 Pixel-wise Gabor Features for Hyperspectral Image Classification 35914.3 Superpixel-wise Gabor Features for HSI Classification 37215 Domain Adaptation for Hyperspectral Image Classification 389Chong Li, Weiwei Sun, Jiangtao Peng, and Kai Ren15.1 Basic Concepts of Domain Adaptation 38915.2 Domain Adaptation for Hyperspectral Image Classification 39015.3 Deep Domain-adaptation-based Hyperspectral Image Classification 39115.4 Conclusion 40216 Unsupervised Domain Adaptation for Classification of Hyperspectral Images 405li Ma and Qian Du16.1 Introduction 40516.2 Unsupervised Domain Adaptation Problem 40816.3 Traditional Unsupervised Domain Adaptation Methods 40816.4 Deep-learning-based Unsupervised Domain Adaptation Methods 41116.5 Experimental Results and Analysis 41416.6 Conclusions 42017 Lightweight Models for Hyperspectral Image Classification 425Hongmin Gao, Shufang Xu, Zhonghao Chen, and Yiyan Zhang17.1 Introduction 42517.2 Lightweight Feature Extraction-based Hyperspectral Image Classification 42717.3 Experimental Results and Analysis 43817.4 Conclusion 44618 Ensemble Method Based Hyperspectral Image Classification 453Wei Feng and Mengdao Xing18.1 Background 45318.2 Introduction to Ensemble Learning 45418.3 Ensemble Learning in HSI Classification 45918.4 Conclusion 46719 Spectral-Spatial Hyperspectral Image Classification Based on Sparse Representation 471Haoyang Yu, Jia Jia, Chuhan Shen, Jiaochan Hu, Chein-I Chang, and Lianru Gao19.1 Introduction 47119.2 Related Models Description 47219.3 Hyperspectral Image Classification Based on Sparse Representation 47519.4 Experimental Results and Analysis 48519.5 Conclusion 49520 Hyperspectral Image Classification with Limited Samples 499Yuebin Wang, Liqiang Zhang, Bing Zhang, Antonio Plaza, and Xiao Xiang Zhu20.1 Introduction 49920.2 Method 50320.3 Experimental Results 50920.4 Conclusions 51921 Constrained Energy Minimization Based Hyperspectral Image Target Detection 521Zhenwei Shi, Zhengxia Zou, Bowen Chen, and Liqin Liu21.1 Introduction 52121.2 Overview of CEM 52221.3 CEM-based Methods 52521.4 Conclusions 53922 Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation 543Wei Li and Kun Gao22.1 Introduction 54322.2 Related Works 54522.3 The Proposed Detection Methodology 54622.4 Experiments and Analysis 55122.5 Conclusion 55823 Weakly Supervised Learning-based Hyperspectral Image Anomaly/Target Detection 561Weiying Xie, Xin Zhang, Yunsong Li, and Qian Du23.1 Introduction 56123.2 Weakly Supervised Hyperspectral Anomaly Detection (WSLRR) 56423.3 Weakly Supervised Hyperspectral Target Detection (BLTSC) 57323.4 Rank-aware Hyperspectral Band Selection (R-GAN) 57823.5 Conclusions 58724 Hyperspectral Anomaly Detection via Background-separable Mode 593Bing Tu, Xianchang Yang, Jun Li, Antonio Plaza, and Kaiyuan Chen24.1 Hyperspectral Anomaly Detection Using Dual Window Density 59324.2 Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis 60324.3 Ensemble Entropy Metric for Hyperspectral Anomaly Detection 61925 Spectral Change Analysis for Multitemporal Change Detection in Hyperspectral Remote Sensing Images 633Sicong Liu, Kecheng Du, Xiaohua Tong, and Peijun Du25.1 Introduction 63325.2 Related Works 63525.3 Spectral Change Analysis in Hyperspectral Images 63725.4 Experimental Setup 64325.5 Results and Analysis 64325.6 Conclusion 650Acknowledgement 65026 Challenges and Future Directions 655Bing Zhang, He Sun, and Ruitong Du26.1 Challenges and Future Directions in Hyperspectral Image Denoising 65526.2 Challenges and Future Directions in Hyperspectral (HS) and Multispectral (MS) Image Fusion 65726.3 Challenges and Future Directions in NMF-based Hyperspectral Unmixing 65926.4 Challenges and Future Directions in Knowledge Graph-enhanced Hyperspectral Unmixing 66026.5 Challenges and Future Directions in Numerical Model-guided Nonlinear Hyperspectral Unmixing 66026.6 Challenges and Future Directions in Hyperspectral Image Classification 66126.7 Chapter on Challenges and Future Directions in Hyperspectral Target Detection 662References 663Index 665

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.

• About the Editor xxiList of Contributors xxiii1 Review for Machine-Learning-Based Hyperspectral Image Analysis 1He Sun and Ruitong Du1.1 Overview 11.2 Denoising 21.3 Super-resolution 41.4 Unmixing 71.5 Classification 81.6 Target Detection 111.7 Change Detection 131.8 Experimental Datasets 151.9 Chapter Arrangement and Writing Purpose 192 Hyperspectral Image Denoising Based on Low-rank Regularization 31Yong Chen, Hongyu Chen, and Wei He2.1 Introduction 312.2 Model-driven Approaches 322.3 Data-driven Approaches 412.4 Conclusion and Outlook 463 Hyperspectral Image Denoising Based on Tensor Models 51Yu-Bang Zheng, Jian-Li Wang, and Xi-Le Zhao3.1 Introduction 513.2 HSI Reconstruction 523.3 Tensor Modeling-based HSI Reconstruction Methods 533.4 Numerical Experiments 663.5 Conclusion 674 Hyperspectral Image Denoising Based on Spatial–Spectral Joint Constraints 73Bin Zhao, Magnus O. Ulfarsson, Jakob Sigurdsson, Jon Atli Benediktsson, and Jocelyn Chanussot4.1 Non-local Means Low-rank Approximation 734.2 Wavelet-based Block Low-rank Representations 794.3 Conclusions 845 Hyperspectral Image Reconstruction Based on Spectral Super-resolution 87Prof. Yanfeng Gu5.1 Introduction 875.2 Experimental Datasets and Evaluation Indicators 905.3 A Learning Subpixel Super-resolution Model Based on Coupled Dictionary 955.4 A Collaborative Spectral-super-resolution Model Based on Adaptive Learning 1065.5 Conclusion 1246 Hyperspectral Image Reconstruction From Supervision to Blindness 129Jie Xie, Jie Wu, Zhicheng Wang, Lina Zhuang, and Leyuan Fang6.1 Introduction 1296.2 Full Supervised HSI SR 1326.3 Weakly Supervised HSI SR 1376.4 Self-supervised HSI SR 1536.5 Blind HSI SR 1696.6 Conclusion and Discussion 1817 Hyperspectral Image Reconstruction Based on Unsupervised Learning 191Ying Qu, Jiangsan Zhao, Hairong Qi, Chiman Kwan, and Liqiang Zhang7.1 Introduction 1917.2 Problem Formulation 1937.3 Unsupervised Hyperspectral Image Super-resolution with Dirichlet Net 1937.4 Unsupervised and Unregistered Hyperspectral Image Super-resolution 1967.5 Improving SR Performance with Endmember-assisted Camera Response Function Learning 2007.6 Conclusions 2018 Hyperspectral Image Reconstruction Based on Adaptive Learning 207Ke Zheng, Jiaxin Li, Lianru Gao, and Bing Zhang8.1 Introduction 2078.2 Problem Formulation 2088.3 Numerical Model-guided Nonlinear Spectral Unmixing 2098.4 Experiment and Results 2188.5 Conclusion 2279 Hyperspectral Unmixing with Nonnegative Matrix Factorization 229Jun Li, Yuanchao Su, Shaoquan Zhang, and Ruoqing Xu9.1 Introduction 2299.2 Methodologies 2309.3 Experiments 2359.4 Conclusion 23910 Hyperspectral Unmixing Based on Low-rank Representation and Sparse Constraint 243Xiangrong Zhang, Jingyan Zhang, Guanchun Wang, and Licheng Jiao10.1 Introduction 24310.2 Linear Unmixing Algorithms 24410.3 Hybrid Unmixing Algorithms 25110.4 Experiments 25710.5 Conclusions 26611 Endmember Purification and Geographical Knowledge Graph-guided Endmember Selection 271Wenfei Luo and Rui Wu11.1 Introduction 27111.2 Endmember Purification 27211.3 Unmixing with Geographic Knowledge Graph 28311.4 Experimental Results and Analysis 28911.5 Conclusion 29812 Hyperspectral Unmixing Based on Deep Autoencoder Networks 301Yuanchao Su, Jun Li, Lianru Gao, Ruoqing Xu, Zhiqing Zhu, and Paolo Gamba12.1 Introduction 30112.2 Methodologies 30212.3 Experimental Results 31412.4 Conclusion 31812.5 Discussion 31813 Numerical-model-guided Nonlinear Spectral Unmixing 321Bin Yang and Bin Wang13.1 Introduction 32113.2 Nonlinear Mixture Models and Extensions 32413.3 Numerical-model-guided Nonlinear Spectral Unmixing 32713.4 Conclusions 34413.5 Challenges and Future Directions 34614 Spatial–Spectral Gabor-based Hyperspectral Image Classification 351Sen Jia, Shuyu Zhang, Qi Ren, Wangquan He, Meng Xu, and Jiasong Zhu14.1 Spatial–Spectral Gabor Feature Extraction 35114.2 Pixel-wise Gabor Features for Hyperspectral Image Classification 35914.3 Superpixel-wise Gabor Features for HSI Classification 37215 Domain Adaptation for Hyperspectral Image Classification 389Chong Li, Weiwei Sun, Jiangtao Peng, and Kai Ren15.1 Basic Concepts of Domain Adaptation 38915.2 Domain Adaptation for Hyperspectral Image Classification 39015.3 Deep Domain-adaptation-based Hyperspectral Image Classification 39115.4 Conclusion 40216 Unsupervised Domain Adaptation for Classification of Hyperspectral Images 405li Ma and Qian Du16.1 Introduction 40516.2 Unsupervised Domain Adaptation Problem 40816.3 Traditional Unsupervised Domain Adaptation Methods 40816.4 Deep-learning-based Unsupervised Domain Adaptation Methods 41116.5 Experimental Results and Analysis 41416.6 Conclusions 42017 Lightweight Models for Hyperspectral Image Classification 425Hongmin Gao, Shufang Xu, Zhonghao Chen, and Yiyan Zhang17.1 Introduction 42517.2 Lightweight Feature Extraction-based Hyperspectral Image Classification 42717.3 Experimental Results and Analysis 43817.4 Conclusion 44618 Ensemble Method Based Hyperspectral Image Classification 453Wei Feng and Mengdao Xing18.1 Background 45318.2 Introduction to Ensemble Learning 45418.3 Ensemble Learning in HSI Classification 45918.4 Conclusion 46719 Spectral-Spatial Hyperspectral Image Classification Based on Sparse Representation 471Haoyang Yu, Jia Jia, Chuhan Shen, Jiaochan Hu, Chein-I Chang, and Lianru Gao19.1 Introduction 47119.2 Related Models Description 47219.3 Hyperspectral Image Classification Based on Sparse Representation 47519.4 Experimental Results and Analysis 48519.5 Conclusion 49520 Hyperspectral Image Classification with Limited Samples 499Yuebin Wang, Liqiang Zhang, Bing Zhang, Antonio Plaza, and Xiao Xiang Zhu20.1 Introduction 49920.2 Method 50320.3 Experimental Results 50920.4 Conclusions 51921 Constrained Energy Minimization Based Hyperspectral Image Target Detection 521Zhenwei Shi, Zhengxia Zou, Bowen Chen, and Liqin Liu21.1 Introduction 52121.2 Overview of CEM 52221.3 CEM-based Methods 52521.4 Conclusions 53922 Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation 543Wei Li and Kun Gao22.1 Introduction 54322.2 Related Works 54522.3 The Proposed Detection Methodology 54622.4 Experiments and Analysis 55122.5 Conclusion 55823 Weakly Supervised Learning-based Hyperspectral Image Anomaly/Target Detection 561Weiying Xie, Xin Zhang, Yunsong Li, and Qian Du23.1 Introduction 56123.2 Weakly Supervised Hyperspectral Anomaly Detection (WSLRR) 56423.3 Weakly Supervised Hyperspectral Target Detection (BLTSC) 57323.4 Rank-aware Hyperspectral Band Selection (R-GAN) 57823.5 Conclusions 58724 Hyperspectral Anomaly Detection via Background-separable Mode 593Bing Tu, Xianchang Yang, Jun Li, Antonio Plaza, and Kaiyuan Chen24.1 Hyperspectral Anomaly Detection Using Dual Window Density 59324.2 Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis 60324.3 Ensemble Entropy Metric for Hyperspectral Anomaly Detection 61925 Spectral Change Analysis for Multitemporal Change Detection in Hyperspectral Remote Sensing Images 633Sicong Liu, Kecheng Du, Xiaohua Tong, and Peijun Du25.1 Introduction 63325.2 Related Works 63525.3 Spectral Change Analysis in Hyperspectral Images 63725.4 Experimental Setup 64325.5 Results and Analysis 64325.6 Conclusion 650Acknowledgement 65026 Challenges and Future Directions 655Bing Zhang, He Sun, and Ruitong Du26.1 Challenges and Future Directions in Hyperspectral Image Denoising 65526.2 Challenges and Future Directions in Hyperspectral (HS) and Multispectral (MS) Image Fusion 65726.3 Challenges and Future Directions in NMF-based Hyperspectral Unmixing 65926.4 Challenges and Future Directions in Knowledge Graph-enhanced Hyperspectral Unmixing 66026.5 Challenges and Future Directions in Numerical Model-guided Nonlinear Hyperspectral Unmixing 66026.6 Challenges and Future Directions in Hyperspectral Image Classification 66126.7 Chapter on Challenges and Future Directions in Hyperspectral Target Detection 662References 663Index 665