1st Edition

Hyperspectral Remote Sensing in Urban Environments

    300 Pages 83 B/W Illustrations
    by CRC Press

    This book is intended to provide a detailed perspective on techniques and challenges in detecting urban materials using hyperspectral data including a systematic perspective on the spectral properties of the materials and methods. It adopts a process chain approach in describing the topic and explains image processing steps from reflectance calibration to final insights. The objective of the book is to provide in-depth information on hyperspectral remote sensing of urban materials covering global case studies as applicable.


    • Covers the complete processing chain of hyperspectral data specifically in urban environments;
    • Gives more information about the mapping and classification of urban scenes;
    • Includes information from basic imaging spectroscopy to advanced methods such as deep learning for imaging spectroscopy;
    • Reviews detailed spectral characteristics of urban materials commonly found in world cities;
    • Discusses advanced supervised methods such as deep learning with a due focus on hyperspectral data analysis.

    This book is aimed at professionals and graduate students in Hyperspectral Imaging, Urban Remote Sensing, and Hyperspectral Image Processing.

    1. Cities Means to Menace?  2. Introduction to Hyperspectral Imaging  3. Reflectance Calibration  4. Spectral Resources  5. Classification of Urban Land Use and Land Cover  6. Appendix


    Shailesh Shankar Deshpande is currently working as Principal Scientist at Tata Research Development and Design Centre, TCS research, Pune, India. He is a part of Robotics and Autonomous Systems (Remote sensing and space-tech division) of TCS research. He has been one of the lead contributors in important artificial intelligence (AI), machine learning (ML), and remote sensing research and solutions TCS research has developed over the years.

    He has an M-tech, and PhD in Hyperspectral Remote Sensing from IIT-Bombay, Mumbai. His main research interest includes processing of hyperspectral and multispectral remotely sensed data. He is particularly interested in using the spectral and spatial properties of the earth observations to generate meaningful insights.

    With research and development experience of nearly 25 years in multidisciplinary areas such as remote sensing and machine learning, he has published over 50 research papers in refereed journals and conferences of international repute. He has authored 5 book chapters and has more than 10 national and international granted patents. A couple of his papers at international conferences have received Best Paper awards. He serves as a reviewer for major remote sensing journals. He has chaired sessions at the international IEEE-WHISPERS 2018 to 2023 conferences, and he is a member of the review committee of IEEE-IGARSS. He has supervised a few B-tech projects and several internships.

    Arun B. Inamdar was at the Centre of Studies in Resources Engineering, IIT-Bombay, Mumbai 400076, India. He has an MSc and PhD in Geology from IIT-Bombay, Mumbai. His research interests include the use of remotely sensed data in various land and ocean applications especially in assessing/monitoring various water quality parameters of the coastal ocean; coastal land use and vulnerability studies; wetlands monitoring; integrated coastal management. He has published over 70 papers and guided 10 doctoral and 20 master's students, besides executing more than 50 research and consultancy projects in various multidisciplinary areas. He has been a reviewer for various national and international journals, including ISRS Journal and Current Science & Environmental Monitoring and Assessment. He was a member of multiple expert committees advising state and national government on various marine and urban environmental issues.