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

1st Edition 

by Anil Kumar (Author), Priyadarshi Upadhyay (Author), Uttara Singh (Author) 

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.