Synopsis "Multi-Sensor and Multi-Temporal Remote Sensing"
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 individual sample as mean training approach to handle heterogeneity within a class. Appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields, and so forth.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, Post disaster, Urban transition studies and other related areas.
