Multi-Sensor and Multi-Temporal Remote Sensing
eBook - ePub

Multi-Sensor and Multi-Temporal Remote Sensing

Specific Single Class Mapping

  1. 148 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Multi-Sensor and Multi-Temporal Remote Sensing

Specific Single Class Mapping

About this book

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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Yes, you can access Multi-Sensor and Multi-Temporal Remote Sensing by Anil Kumar,Priyadarshi Upadhyay,Uttara Singh in PDF and/or ePUB format, as well as other popular books in Computer Science & Civil Engineering. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Half Title
  3. Title
  4. Copyright
  5. Dedication
  6. Contents
  7. Foreword
  8. Preface
  9. Our Gratitude with three Rs
  10. Author Biographies
  11. List of Abbreviations
  12. Chapter 1 Remote-Sensing Images
  13. Chapter 2 Evolution of Pixel-Based Spectral Indices
  14. Chapter 3 Multi-Sensor, Multi-Temporal Remote-Sensing
  15. Chapter 4 Training Approaches—Role of Training Data
  16. Chapter 5 Machine-Learning Models for Specific-Class Mapping
  17. Chapter 6 Learning-Based Algorithms for Specific-Class Mapping
  18. Appendix A1 Specific Single Class Mapping Case Studies
  19. Appendix A2 SMIC—Temporal Data-Processing Module for Specific-Class Mapping
  20. Index