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 Informatik & Informatik Allgemein. We have over one million books available in our catalogue for you to explore.

Information

Publisher

CRC Press

Year

2023

ISBN

9781000872200

Edition

Topic

Informatik

Subtopic

Informatik Allgemein

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