Description

Book Synopsis

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 âindividual sample as meanâ 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.

MultiSensor and MultiTemporal Remote Sensing

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    A Paperback by Anil Kumar

    £43.69

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    RRP £45.99 – you save £2.30 (5%)

    Order before 4pm tomorrow for delivery by Fri 25 Sep 2026.

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      Book details

      Publisher Taylor & Francis
      Imprint Taylor & Francis Ltd
      Published 1/30/2025
      ISBN-13 9781032446523
      978-1032446523
      ISBN-10 1032446528
      Also in:
      Instruments and instrumentation

      Description

      Book Synopsis

      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 âindividual sample as meanâ 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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