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

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.

1. Remote-Sensing Images 2. Evolution of Pixel-Based Spectral Indices 3. Multi-Sensor, Multi-Temporal Remote-Sensing 4. Training ApproachesRole 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 SMICTemporal Data-Processing Module for Specific-Class Mapping

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 forló%