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<p>EDITOR BIOGRAPHY vii</p> <p>LIST OF CONTRIBUTORS viii</p> <p>PREFACE x</p> <p><b>PART I GENERAL THEORY 1</b></p> <p>1 Introduction: Two Fundamental Principles Behind Hyperspectral Imaging 3<br /><i>Chein-I Chang</i></p> <p>2 Overview of Hyperspectral Imaging Remote Sensing from Satellites 41<br /><i>Shen-En Qian</i></p> <p>3 Efficient Hardware Implementation for Hyperspectral Anomaly and Target Detection 67<br /><i>Jie Lei, Weiying Xie, Jiaojiao Li, Keyan Wang, Kai Liu, and Yunsong Li</i></p> <p><b>PART II BAND SELECTION FOR HYPERSPECTRAL IMAGING 107</b></p> <p>4 Constrained Band Selection for Hyperspectral Imaging 109<br /><i>Chein-I Chang</i></p> <p>5 Band Subset Selection for Hyperspectral Imaging 147<br /><i>Chein-I Chang</i></p> <p>6 Progressive Band Selection Processing for Hyperspectral Image Classification 179<br /><i>Chunyan Yu, Meiping Song, and Chein-I Chang</i></p> <p><b>PART III COMPRESSIVE SENSING FOR HYPERSPECTRAL IMAGING 205</b></p> <p>7 Restricted Entropy and Spectrum Properties for Hyperspectral Imaging 207<br /><i>Chein-I Chang and Bernard Lampe</i></p> <p>8 Endmember Finding in Compressively Sensed Band Domain 228<br /><i>Chein-I Chang and Adam Bekit</i></p> <p>9 Hyperspectral Image Classification in Compressively Sensed Band Domain 252<br /><i>Charles J. Della-Porta and Chein-I Chang</i></p> <p><b>PART IV FUSION FOR HYPERSPECTRAL IMAGING 279</b></p> <p>10 Hyperspectral and LiDAR Data Fusion 281<br /><i>Qian Du, Wei Li, and Chiru Ge</i></p> <p>11 Hyperspectral Data Fusion Using Multidimensional Information 293<br /><i>Lifu Zhang, Xia Zhang, Mingyuan Peng, Xuejian Sun, and Xiaoyang Zhao</i></p> <p>12 Fusion of Band Selection Methods for Hyperspectral Imaging 341<br /><i>Yulei Wang, Lin Wang, and Chein-I Chang</i></p> <p><b>PART V HYPERSPECTRAL DATA UNMIXING 363</b></p> <p>13 Model-Inspired Deep Neural Networks for Hyperspectral Unmixing 365<br /><i>Yuntao Qian, Fengchao Xiong, Minchao Ye, and Jun Zhou</i></p> <p>14 Analytical Fully Constrained Least Squares Linear Spectral Mixture Analysis 404<br /><i>Chein-I Chang and Hsiao-Chi Li</i></p> <p>15 Swarm Intelligence Optimization-Based Spectral Unmixing 422<br /><i>Lianru Gao, Xu Sun, Zhu Han, Lina Zhuang, Wenfei Luo, and Bing Zhang</i></p> <p>16 Spectral-Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing 453<br /><i>Risheng Huang, Xiaorun Li, and Liaoying Zhao</i></p> <p><b>PART VI HYPERSPECTRAL IMAGE CLASSIFICATION 483</b></p> <p>17 Sparse Representation-Based Hyperspectral Image Classification 485<br /><i>Haoyang Yu, Jun Li, Wei Li, and Bing Zhang</i></p> <p>18 Collaborative Classification Based on Hyperspectral Images 506<br /><i>Junping Zhang, Xiaochen Lu, and Tong Li</i></p> <p>19 Class Feature-Weighted Hyperspectral Image Classification 543<br /><i>Shengwei Zhong, Jiaojiao Li, Xiaodi Shang, Shuhan Chen, and Chein-I Chang</i></p> <p>20 Target Detection Approaches to Hyperspectral Image Classification 565<br /><i>Chein-I Chang, Bai Xue, and Chunyan Yu</i></p> <p>INDEX 586</p>

<p><b>Chein-I Chang, PhD, </b>is 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.

<p><b>Authoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications</b> <p><i>Advances in Hyperspectral Image Processing Techniques</i> 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. <p>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. <p>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. <p>Specific sample topics covered in <i>Advances in Hyperspectral Image Processing Techniques</i> include: <ul><li>Two fundamental principles of hyperspectral imaging</li> <li>Constrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification</li> <li>Restricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain</li> <li>Hyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information</li> <li>Advances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis</li> <li>Sparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification</li></ul> <p>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, <i>Advances in Hyperspectral Image Processing Techniques</i> is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field.

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