Hyperspectral Remote Sensing in Urban Environments - Bookswagon
Hyperspectral Remote Sensing in Urban Environments

Hyperspectral Remote Sensing in Urban Environments


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About the Book

This book is intended to provide a detailed perspective on techniques and challenges in detecting the urban materials using hyperspectral data including systematic perspective on spectral properties of the materials and methods. It adopts 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.

Features:

  • Covers complete processing chain of hyperspectral data specifically in urban environment
  • Gives more information about the mapping and classification of urban scenes
  • Includes information from basic imaging spectroscopy to the advanced methods such as deep learning for imaging spectroscopy
  • Reviews detailed spectral characteristics of urban materials commonly found in world cities
  • Discuses advanced supervised methods such as deep learning with 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.
About the Author:

Shailesh S. 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 spec-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 M-tech, and Ph.D. 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 spectral and spatial properties of the earth observations to generate meaningful insights.With a research and development experience of nearly 25 years in multidisciplinary areas such as remote sensing, machine learning, he has published over 50 research papers in refereed journals and conferences of international repute. He has authored 5 book chapters and have 10+ national and international granted patents. Couple of his papers in international conference have received best paper awards. He serves as a reviewer for major remote sensing journals. He has chaired sessions at international IEEE-WHISPERS 2018 to 23 conferences, and he is a member of review committee of IEEE-IGARSS. He has supervised a few B-tech projects and several internships.

Professor (Retd.), Arun B. Inamdar was at Centre of Studies in Resources Engineering, IIT-Bombay, Mumbai 400076, India. He has M. Sc. Ph.D. in geology from IIT-Bombay, Mumbai. His research interests include use of remotely sensed data in various land and ocean applications esp. 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, guided 10 doctoral and 20 masters students, besides executing around 50+ research and consultancy projects in various multidisciplinary areas. He has been a reviewer for various national and international journals including, ISRS Journal, 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.


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Product Details
  • ISBN-13: 9781032359106
  • Publisher: CRC Press
  • Publisher Imprint: Crc Press
  • Height: 234 mm
  • No of Pages: 328
  • Spine Width: 18 mm
  • Width: 156 mm
  • ISBN-10: 1032359102
  • Publisher Date: 02 Oct 2023
  • Binding: Hardback
  • Language: English
  • Returnable: N
  • Weight: 648 gr


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Hyperspectral Remote Sensing in Urban Environments
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Hyperspectral Remote Sensing in Urban Environments
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