Advances in Hyperspectral Image Processing Techniques

Herausgegeben:Chang, Chein-I

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Wiley - IEEE
• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119687760
• 1. Auflage
• Seitenzahl: 608
• Erscheinungstermin: 22. November 2022
• Englisch
• Abmessung: 260mm x 183mm x 37mm
• Gewicht: 1180g
• ISBN-13: 9781119687764
• ISBN-10: 1119687764
• Artikelnr.: 60416897

Autorenporträt

Chein-I Chang, PhD, 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.

Inhaltsangabe

EDITOR BIOGRAPHY vii

LIST OF CONTRIBUTORS viii

PREFACE x

PART I GENERAL THEORY 1

1 Introduction: Two Fundamental Principles Behind Hyperspectral Imaging 3
Chein-I Chang

2 Overview of Hyperspectral Imaging Remote Sensing from Satellites 41
Shen-En Qian

3 Efficient Hardware Implementation for Hyperspectral Anomaly and Target Detection 67
Jie Lei, Weiying Xie, Jiaojiao Li, Keyan Wang, Kai Liu, and Yunsong Li

PART II BAND SELECTION FOR HYPERSPECTRAL IMAGING 107

4 Constrained Band Selection for Hyperspectral Imaging 109
Chein-I Chang

5 Band Subset Selection for Hyperspectral Imaging 147
Chein-I Chang

6 Progressive Band Selection Processing for Hyperspectral Image Classification 179
Chunyan Yu, Meiping Song, and Chein-I Chang

PART III COMPRESSIVE SENSING FOR HYPERSPECTRAL IMAGING 205

7 Restricted Entropy and Spectrum Properties for Hyperspectral Imaging 207
Chein-I Chang and Bernard Lampe

8 Endmember Finding in Compressively Sensed Band Domain 228
Chein-I Chang and Adam Bekit

9 Hyperspectral Image Classification in Compressively Sensed Band Domain 252
Charles J. Della-Porta and Chein-I Chang

PART IV FUSION FOR HYPERSPECTRAL IMAGING 279

10 Hyperspectral and LiDAR Data Fusion 281
Qian Du, Wei Li, and Chiru Ge

11 Hyperspectral Data Fusion Using Multidimensional Information 293
Lifu Zhang, Xia Zhang, Mingyuan Peng, Xuejian Sun, and Xiaoyang Zhao

12 Fusion of Band Selection Methods for Hyperspectral Imaging 341
Yulei Wang, Lin Wang, and Chein-I Chang

PART V HYPERSPECTRAL DATA UNMIXING 363

13 Model-Inspired Deep Neural Networks for Hyperspectral Unmixing 365
Yuntao Qian, Fengchao Xiong, Minchao Ye, and Jun Zhou

14 Analytical Fully Constrained Least Squares Linear Spectral Mixture Analysis 404
Chein-I Chang and Hsiao-Chi Li

15 Swarm Intelligence Optimization-Based Spectral Unmixing 422
Lianru Gao, Xu Sun, Zhu Han, Lina Zhuang, Wenfei Luo, and Bing Zhang

16 Spectral-Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing 453
Risheng Huang, Xiaorun Li, and Liaoying Zhao

PART VI HYPERSPECTRAL IMAGE CLASSIFICATION 483

17 Sparse Representation-Based Hyperspectral Image Classification 485
Haoyang Yu, Jun Li, Wei Li, and Bing Zhang

18 Collaborative Classification Based on Hyperspectral Images 506
Junping Zhang, Xiaochen Lu, and Tong Li

19 Class Feature-Weighted Hyperspectral Image Classification 543
Shengwei Zhong, Jiaojiao Li, Xiaodi Shang, Shuhan Chen, and Chein-I Chang

20 Target Detection Approaches to Hyperspectral Image Classification 565
Chein-I Chang, Bai Xue, and Chunyan Yu

INDEX 586