Hyperspectral Remote Sensing

Theory and Applications

Prem Chandra Pandey, Prashant K. Srivastava, Heiko Balzter, Bimal Bhattacharya, George P. Petropoulos

• Publisher: Elsevier
• Year: 2020
• Print ISBN: 9780081028940
• eBook ISBN: 9780081028957
• Topic: Physical Sciences
• Subtopic: Geophysics

Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology.- Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines- Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection- Provides an overview of the state-of-the-art, including algorithms, techniques and case studies

Section I

Introduction to Hyperspectral Remote Sensing and Principles of Theory and Data Processing

Outline

• 1 Revisiting hyperspectral remote sensing: origin, processing, applications and way forward
• 2 Spectral smile correction for airborne imaging spectrometers
• 3 Anomaly detection in hyperspectral remote sensing images
• 4 Atmospheric parameter retrieval and correction using hyperspectral data
• 5 Hyperspectral image classifications and feature selection

1

Revisiting hyperspectral remote sensing: origin, processing, applications and way forward

Prashant K. Srivastava^{1, 2}, Ramandeep Kaur M. Malhi¹, Prem Chandra Pandey³, Akash Anand¹, Prachi Singh¹, Manish Kumar Pandey¹ and Ayushi Gupta¹, ¹1Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India, ²2DST-Mahamana Centre for Excellence in Climate Change Research, Banaras Hindu University, Varanasi, India, ³3Center for Environmental Sciences & Engineering, School of Natural Sciences, Shiv Nadar University, Greater, Noida, India

Abstract

After several years of research and development in hyperspectral imaging systems that enriched our knowledge and enhanced our capacity to explore the Earth, these systems have been widely accepted by the remote sensing community. They have evolved as major techniques and have now entered the mainstream of the earth observation data users. This chapter discusses the origin of hyperspectral remote sensing, its importance, preprocessing, inversion models suitable for hyperspectral datasets, as well as several possible applications, including but not limited to, vegetation analysis, agriculture, urban, water quality, and mineral identification. The chapter concludes by looking at the way forward for hyperspectral remote sensing.

Keywords

Inversion models; atmospheric corrections; hyperspectral applications; challenges; ultraspectral

1.1 Introduction

Multispectral remote sensors with a few broad spectral bands were evolved during the 1970s to monitor natural resources. Looking at the existing limitations of multispectral images in different applications and recognizing the demand for better and more advanced images with higher spectral resolution, there was an urgent need for research and development of hyperspectral imaging systems. Hyperspectral remote sensing is an output of imaging spectroscopy that is facilitated by rapid advancement in technologies and the development of detectors, optical design and components, atmospheric radiative transfer and processing capability. Imaging spectroscopy, in turn, utilizes two sensing techniques, namely spectroscopy and imaging. An imaging system captures the spatial distribution of a scene and measures the relative concentration of the objects, while spectroscopy offers the ability to differentiate the elusive absorption features of divergent materials for a scene. The initial or conventional approaches of using multispectral or broadband sensors has the limitation of dividing a discontinuous spectral coverage into numerous broadbands. Hyperspectral remote sensing gained growth and popularity because it collects data that span over a vast region in umpteen contiguous narrow spectral bands of the electromagnetic spectrum, ranging from visible (VIS)–near-infrared (NIR) to shortwave infrared (SWIR) and can achieve a spectral resolution of 10⁻²λ. Based on platform type, hyperspectral remotely sensed data can be classified as non-imaging or imaging in situ measurements, airborne images, and space-borne images.

1.2 Origin of hyperspectral remote sensing

A landmark step was achieved in 1979 when hybrid array detectors, mercury cadmium telluride on silicon charge-coupled devices was made available for the first time leading to the construction of an imaging spectrometer that operated at wavelengths beyond 1.0 of μm. The airborne imaging spectrometer (AIS) was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) in 1983 and operated at wavelengths between 0.8 and 2.5 μm. AIS was replaced by the airborne visible/infrared imaging spectrometer (AVIRIS) in the early 1990s that covered the entire spectrum from 0.4 μm to 2.45 μm at a high spectral rate plus high spatial resolution over an 11 km swath. The primary objectives of these missions were identifying and assessing the characteristics of surface materials. It was with AVIRIS data that the first vegetation analysis was carried out through near-infrared spectroscopy (NIRS) analysis by John Aber and Mary Martin of the University of New Hampshire. Although AVIRIS has offered the bulk of high-quality hyperspectral data, it lacks regularity.

With known multispectral specifications and properties, one wants to gain information from spectral information, and there must be several narrow spec...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Biography
• Foreword
• Preface
• Section I: Introduction to Hyperspectral Remote Sensing and Principles of Theory and Data Processing
• Section II: Hyperspectral Remote Sensing Application in Vegetation
• Section III: Hyperspectral Remote Sensing Application in Water, Snow, Urban Research
• Section IV: Hyperspectral Remote Sensing Application in Soil and Mineral Exploration
• Section V: Hyperspectral Remote Sensing: Multi-sensor, Fusion and Indices applications for Pollution Detection and Other Applications
• Section VI: Hyperspectral Remote Sensing: Challenges, Future Pathway for Research & Emerging Applications
• Author Index
• Subject Index