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.

 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 better understanding of hyperspectral techniques.

> **Jie Chen** Centre of Intelligent Acoustic and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechnical University, China

> > **Yingying Song** Centre de Recherche en Automatique de Nancy (CRAN), CNRS, University of Lorraine, France

## **Hengchao Li**

Section 1

Theoretical Advances of

Hyperspectral Image

Processing

1

Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, China

## Section 1
