Multi-sensor And Multi-temporal Remote Sensing Av Anil (london Metropolitan University Uk) Kumar, Priyadarshi (uttarakhand Space Application Centre In
<P>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.</P><P>Key features:</P><UL><UL><P></P></UL><LI>Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes</LI><LI>Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise</LI><LI>Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) </LI><LI>Discusses the role of training data to handle the heterogeneity withi
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