{"product_id":"advances-in-hyperspectral-image-processing-techniques-isbn-9781119687764","title":"Advances in Hyperspectral Image Processing Techniques","description":"\u003cb\u003eAdvances in Hyperspectral Image Processing Techniques\u003c\/b\u003e \u003cp\u003e\u003cb\u003eAuthoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAdvances in Hyperspectral Image Processing Techniques\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eSpecific sample topics covered in \u003ci\u003eAdvances in Hyperspectral Image Processing Techniques\u003c\/i\u003e include: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eTwo fundamental principles of hyperspectral imaging\u003c\/li\u003e \u003cli\u003eConstrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification\u003c\/li\u003e \u003cli\u003eRestricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain\u003c\/li\u003e \u003cli\u003eHyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information\u003c\/li\u003e \u003cli\u003eAdvances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis\u003c\/li\u003e \u003cli\u003eSparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWith 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, \u003ci\u003eAdvances in Hyperspectral Image Processing Techniques\u003c\/i\u003e is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field. \u003c\/p\u003e\u003cp\u003eEDITOR BIOGRAPHY vii\u003c\/p\u003e \u003cp\u003eLIST OF CONTRIBUTORS viii\u003c\/p\u003e \u003cp\u003ePREFACE x\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I GENERAL THEORY 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction: Two Fundamental Principles Behind Hyperspectral Imaging 3\u003cbr\u003e\u003ci\u003eChein-I Chang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Overview of Hyperspectral Imaging Remote Sensing from Satellites 41\u003cbr\u003e\u003ci\u003eShen-En Qian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Efficient Hardware Implementation for Hyperspectral Anomaly and Target Detection 67\u003cbr\u003e\u003ci\u003eJie Lei, Weiying Xie, Jiaojiao Li, Keyan Wang, Kai Liu, and Yunsong Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II BAND SELECTION FOR HYPERSPECTRAL IMAGING 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4 Constrained Band Selection for Hyperspectral Imaging 109\u003cbr\u003e\u003ci\u003eChein-I Chang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Band Subset Selection for Hyperspectral Imaging 147\u003cbr\u003e\u003ci\u003eChein-I Chang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Progressive Band Selection Processing for Hyperspectral Image Classification 179\u003cbr\u003e\u003ci\u003eChunyan Yu, Meiping Song, and Chein-I Chang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III COMPRESSIVE SENSING FOR HYPERSPECTRAL IMAGING 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7 Restricted Entropy and Spectrum Properties for Hyperspectral Imaging 207\u003cbr\u003e\u003ci\u003eChein-I Chang and Bernard Lampe\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Endmember Finding in Compressively Sensed Band Domain 228\u003cbr\u003e\u003ci\u003eChein-I Chang and Adam Bekit\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Hyperspectral Image Classification in Compressively Sensed Band Domain 252\u003cbr\u003e\u003ci\u003eCharles J. Della-Porta and Chein-I Chang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV FUSION FOR HYPERSPECTRAL IMAGING 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10 Hyperspectral and LiDAR Data Fusion 281\u003cbr\u003e\u003ci\u003eQian Du, Wei Li, and Chiru Ge\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Hyperspectral Data Fusion Using Multidimensional Information 293\u003cbr\u003e\u003ci\u003eLifu Zhang, Xia Zhang, Mingyuan Peng, Xuejian Sun, and Xiaoyang Zhao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Fusion of Band Selection Methods for Hyperspectral Imaging 341\u003cbr\u003e\u003ci\u003eYulei Wang, Lin Wang, and Chein-I Chang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V HYPERSPECTRAL DATA UNMIXING 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13 Model-Inspired Deep Neural Networks for Hyperspectral Unmixing 365\u003cbr\u003e\u003ci\u003eYuntao Qian, Fengchao Xiong, Minchao Ye, and Jun Zhou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Analytical Fully Constrained Least Squares Linear Spectral Mixture Analysis 404\u003cbr\u003e\u003ci\u003eChein-I Chang and Hsiao-Chi Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Swarm Intelligence Optimization-Based Spectral Unmixing 422\u003cbr\u003e\u003ci\u003eLianru Gao, Xu Sun, Zhu Han, Lina Zhuang, Wenfei Luo, and Bing Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 Spectral-Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing 453\u003cbr\u003e\u003ci\u003eRisheng Huang, Xiaorun Li, and Liaoying Zhao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VI HYPERSPECTRAL IMAGE CLASSIFICATION 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17 Sparse Representation-Based Hyperspectral Image Classification 485\u003cbr\u003e\u003ci\u003eHaoyang Yu, Jun Li, Wei Li, and Bing Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 Collaborative Classification Based on Hyperspectral Images 506\u003cbr\u003e\u003ci\u003eJunping Zhang, Xiaochen Lu, and Tong Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19 Class Feature-Weighted Hyperspectral Image Classification 543\u003cbr\u003e\u003ci\u003eShengwei Zhong, Jiaojiao Li, Xiaodi Shang, Shuhan Chen, and Chein-I Chang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 Target Detection Approaches to Hyperspectral Image Classification 565\u003cbr\u003e\u003ci\u003eChein-I Chang, Bai Xue, and Chunyan Yu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eINDEX 586\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChein-I Chang, PhD, \u003c\/b\u003eis a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County (UMBC). He is a Life Fellow of IEEE and a Fellow of SPIE. He is an Associate Editor of Remote Sensing and IEEE Transaction on Geoscience and Remote Sensing. Dr. Chang has authored four books, edited two books, and co-edited one book.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAuthoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAdvances in Hyperspectral Image Processing Techniques\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eSpecific sample topics covered in \u003ci\u003eAdvances in Hyperspectral Image Processing Techniques\u003c\/i\u003e include: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eTwo fundamental principles of hyperspectral imaging\u003c\/li\u003e \u003cli\u003eConstrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification\u003c\/li\u003e \u003cli\u003eRestricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain\u003c\/li\u003e \u003cli\u003eHyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information\u003c\/li\u003e \u003cli\u003eAdvances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis\u003c\/li\u003e \u003cli\u003eSparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWith 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, \u003ci\u003eAdvances in Hyperspectral Image Processing Techniques\u003c\/i\u003e is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47988683407589,"sku":"NP9781119687764","price":175.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119687764.jpg?v=1761781226","url":"https:\/\/k12savings.com\/products\/advances-in-hyperspectral-image-processing-techniques-isbn-9781119687764","provider":"K12savings","version":"1.0","type":"link"}