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## Meet the editors

Dr. Jie Chen is currently a professor at the Center of Intelligent Acoustics and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechnical University. He has conducted research at the University of Nice Sophia-Antipolis, France, and the University of Michigan, Ann Abor, USA, from 2009 to 2014. His research interests include distributed optimization, stochastic signal processing, machine learning with

applications to acoustic signal processing, and image processing. Dr. Chen served as Technical Program Co-Chair of the International Workshop on Acoustic Echo and Noise Control (2016), Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (2018–2019), and Program Co-Chair of the Intelligent Signal and Information Processing Summer School (2019) of the IEEE Signal Processing Society.

Dr. Yingying Song received her Dipl-Ing degree in System, Network and Telecommunication Engineering from the University of Technology of Troyes, France, in 2015, and her PhD degree from the Université de Lorraine, France, in 2018. She has worked at the Centre de Recherche en Automatique de Nancy, University of Lorraine, France. Her current research interests include hyperspectral image deconvolution, adaptive image processing,

and hyperspectral image unmixing.

Dr. Hengchao Li is currently a professor at the Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China. He was a visiting scholar at the University of Colorado at Boulder, Boulder, CO, USA, during 2014. His research interests include statistical analysis of synthetic aperture radar images, remote sensing image processing, and signal processing in communications. Dr. Li was

awarded the New Century Excellent Talents in University from the Ministry of Education of China in 2011. In addition, he has also been a reviewer for several international journals and conferences. He is currently serving as an associate editor of the *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing*.

Contents

**Section 1**

Methods

**Section 2**

*by Xian-Hua Han*

*and Carlos Lopez-Franco*

Tea Buds during Dehydration

*by Mbongowo Mbuh*

**Preface III**

Theoretical Advances of Hyperspectral Image Processing **1**

**Chapter 1 3**

**Chapter 2 23**

**Chapter 3 49** Hyperspectral Image Super-Resolution Using Optimization and DCNN-Based

**Chapter 4 73**

Applications of Hyperspectral Image Processing **91**

**Chapter 5 93** NIR Hyperspectral Imaging for Mapping of Moisture Content Distribution in

**Chapter 6 107**

*by Carlos Villaseñor, Javier Gomez-Avila, Nancy Arana-Daniel, Alma Y. Alanis* 

*by Karbhari V. Kale, Mahesh M. Solankar and Dhananjay B. Nalawade*

Hyperspectral Endmember Extraction Techniques

Fast Chaotic Encryption for Hyperspectral Images

*by Keqiang Yu, Yanru Zhao, Xiaoli Li and Yong He*

Use of Hyperspectral Remote Sensing to Estimate Water Quality

Hyperspectral Image Classification *by Rajesh Gogineni and Ashvini Chaturvedi*

## Contents


Preface

Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This emphasizes the importance of using advanced signal processing, image processing, and machine learning techniques for such a purpose. Classical hyperspectral image analysis tasks include target detection, classification, and spectral unmixing. This book intends to provide a comprehensive overview of the recent state of the art of these tasks. Thereafter, considering the prosperous study of deep-learning-based image and data analysis, this book also aims to collect the latest results of hyperspectral data analysis that benefit from deep neural networks. Finally, practical applications will be included to show how these analyses are useful

in promoting real industry, medical, and biological development.

better understanding of hyperspectral techniques.

The book covers two sections, namely, Theoretical Advances of Hyperspectral Image Processing and Applications of Hyperspectral Image Processing. In the first section, the chapters "Hyperspectral Endmember Extraction Techniques" and "Hyperspectral Image Classification" present typical techniques, both classical and deep-learning based, for unmixing and classification tasks. The chapter "Hyperspectral Image Super-Resolution Using Optimization and DCNN-Based Methods" presents optimization-based and deep-learning-based super-resolution techniques. The chapter "Fast Chaotic Encryption for Hyperspectral Images" considers another fundamental but important aspect, i.e., the encryption of data. The second section includes two application-oriented chapters. Hyperspectral techniques are used for evaluating the quality of tea and water, respectively, in "NIR Hyperspectral Imaging for Mapping of Moisture Content Distribution in Tea Buds During Dehydration" and "Use of Hyperspectral Remote Sensing to Estimate Water Quality." The editors believe that readers can benefit from these chapters and gain a

Centre of Intelligent Acoustic and Immersive Communications,

Centre de Recherche en Automatique de Nancy (CRAN),

School of Marine Science and Technology, Northwestern Polytechnical University,

**Jie Chen**

China

France

**Yingying Song**

CNRS, University of Lorraine,
