Advances in Hyperspectral Image Processing Techniques Authoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications Advances in Hyperspectral Image Processing Techniques is derived from recent developments of hyperspectral imaging (HSI) techniques along with new applications in the field, covering many new ideas that have been explored and have led to various new directions in the past few years. The work gathers an array of disparate research into one resource and explores its numerous applications across a wide variety of disciplinary areas. In particular, it includes an introductory chapter on fundamentals of HSI and a chapter on extensive use of HSI techniques in satellite on-orbit and on-board processing to aid readers involved in these specific fields. The book s content is based on the expertise of invited scholars and is categorized into six parts. Part I provides general theory. Part II presents various Band Selection techniques for Hyperspectral Images. Part III reviews recent developments on Compressive Sensing for Hyperspectral Imaging. Part IV includes Fusion of Hyperspectral Images. Part V covers Hyperspectral Data Unmixing. Part VI offers different views on Hyperspectral Image Classification. Specific sample topics covered in Advances in Hyperspectral Image Processing Techniques include: Two fundamental principles of hyperspectral imaging Constrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification Restricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain Hyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information Advances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis Sparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification With many applications beyond traditional remote sensing, ranging from defense and intelligence, to agriculture, to forestry, to environmental monitoring, to food safety and inspection, to medical imaging, Advances in Hyperspectral Image Processing Techniques is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field.

Advances in Hyperspectral Image Processing TechniquesAuthoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applicationsAdvances in Hyperspectral Image Processing Techniques is derived from recent developments of hyperspectral imaging (HSI) techniques along with new applications in the field, covering many new ideas that have been explored and have led to various new directions in the past few years.The work gathers an array of disparate research into one resource and explores its numerous applications across a wide variety of disciplinary areas. In particular, it includes an introductory chapter on fundamentals of HSI and a chapter on extensive use of HSI techniques in satellite on-orbit and on-board processing to aid readers involved in these specific fields.The book's content is based on the expertise of invited scholars and is categorized into six parts. Part I provides general theory. Part II presents various Band Selection techniques for Hyperspectral Images. Part III reviews recent developments on Compressive Sensing for Hyperspectral Imaging. Part IV includes Fusion of Hyperspectral Images. Part V covers Hyperspectral Data Unmixing. Part VI offers different views on Hyperspectral Image Classification.Specific sample topics covered in Advances in Hyperspectral Image Processing Techniques include:* Two fundamental principles of hyperspectral imaging* Constrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification* Restricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain* Hyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information* Advances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis* Sparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classificationWith many applications beyond traditional remote sensing, ranging from defense and intelligence, to agriculture, to forestry, to environmental monitoring, to food safety and inspection, to medical imaging, Advances in Hyperspectral Image Processing Techniques is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field.

EDITOR BIOGRAPHY viiLIST OF CONTRIBUTORS viiiPREFACE xPART I GENERAL THEORY 11 Introduction: Two Fundamental Principles Behind Hyperspectral Imaging 3Chein-I Chang2 Overview of Hyperspectral Imaging Remote Sensing from Satellites 41Shen-En Qian3 Efficient Hardware Implementation for Hyperspectral Anomaly and Target Detection 67Jie Lei, Weiying Xie, Jiaojiao Li, Keyan Wang, Kai Liu, and Yunsong LiPART II BAND SELECTION FOR HYPERSPECTRAL IMAGING 1074 Constrained Band Selection for Hyperspectral Imaging 109Chein-I Chang5 Band Subset Selection for Hyperspectral Imaging 147Chein-I Chang6 Progressive Band Selection Processing for Hyperspectral Image Classification 179Chunyan Yu, Meiping Song, and Chein-I ChangPART III COMPRESSIVE SENSING FOR HYPERSPECTRAL IMAGING 2057 Restricted Entropy and Spectrum Properties for Hyperspectral Imaging 207Chein-I Chang and Bernard Lampe8 Endmember Finding in Compressively Sensed Band Domain 228Chein-I Chang and Adam Bekit9 Hyperspectral Image Classification in Compressively Sensed Band Domain 252Charles J. Della-Porta and Chein-I ChangPART IV FUSION FOR HYPERSPECTRAL IMAGING 27910 Hyperspectral and LiDAR Data Fusion 281Qian Du, Wei Li, and Chiru Ge11 Hyperspectral Data Fusion Using Multidimensional Information 293Lifu Zhang, Xia Zhang, Mingyuan Peng, Xuejian Sun, and Xiaoyang Zhao12 Fusion of Band Selection Methods for Hyperspectral Imaging 341Yulei Wang, Lin Wang, and Chein-I ChangPART V HYPERSPECTRAL DATA UNMIXING 36313 Model-Inspired Deep Neural Networks for Hyperspectral Unmixing 365Yuntao Qian, Fengchao Xiong, Minchao Ye, and Jun Zhou14 Analytical Fully Constrained Least Squares Linear Spectral Mixture Analysis 404Chein-I Chang and Hsiao-Chi Li15 Swarm Intelligence Optimization-Based Spectral Unmixing 422Lianru Gao, Xu Sun, Zhu Han, Lina Zhuang, Wenfei Luo, and Bing Zhang16 Spectral-Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing 453Risheng Huang, Xiaorun Li, and Liaoying ZhaoPART VI HYPERSPECTRAL IMAGE CLASSIFICATION 48317 Sparse Representation-Based Hyperspectral Image Classification 485Haoyang Yu, Jun Li, Wei Li, and Bing Zhang18 Collaborative Classification Based on Hyperspectral Images 506Junping Zhang, Xiaochen Lu, and Tong Li19 Class Feature-Weighted Hyperspectral Image Classification 543Shengwei Zhong, Jiaojiao Li, Xiaodi Shang, Shuhan Chen, and Chein-I Chang20 Target Detection Approaches to Hyperspectral Image Classification 565Chein-I Chang, Bai Xue, and Chunyan YuINDEX 586