Multi-Sensor and Multi-Temporal Remote Sensing: Specific Single Class Mapping, 1st Edition

.\nThis 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.\nMulti-Sensor and Multi-Temporal Remote Sensing: Specific Single Class Mapping 1st Edition is written by Anil Kumar; Priyadarshi Upadhyay; Uttara Singh and published by CRC Press. ISBNs for Multi-Sensor and Multi-Temporal Remote Sensing are 9781000872200, 1000872203 and the print ISBNs are 9781032428321, 1032428325. Additional ISBNs include 9781003373216, 9781032446523, 9781000872194.