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Full Description
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.
Contents
1. Remote-Sensing Images 2. Evolution of Pixel-Based Spectral Indices 3. Multi-Sensor, Multi-Temporal Remote-Sensing 4. Training Approaches—Role of Training Data 5. Machine-Learning Models for Specific-Class Mapping 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
