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Multi-Sensor and Multi-Temporal Remote Sensing : Specific Single Class Mapping

By: (Author) Anil Kumar , (Author) Priyadarshi Upadhyay , (Author) Uttara Singh

Manufacture on Demand

Ksh 15,300.00

Format: Hardback or Cased Book

ISBN-10: 1032428325

ISBN-13: 9781032428321

Publisher: Taylor & Francis Ltd

Imprint: CRC Press

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Apr 17th, 2023

Print length: 148 Pages

Weight: 386 grams

Dimensions (height x width x thickness): 24.00 x 16.40 x 1.60 cms

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  • Description

  • Reviews

This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, fuzzy machine learning models supported by case studies.

This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields.

Key features:

  • Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes
  • Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise
  • Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI)
  • Discusses the role of training data to handle the heterogeneity within a class
  • Supports multi-sensor and multi-temporal data processing through in-house SMIC software
  • Includes case studies and practical applications for single class mapping

This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.


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