Author Description

<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> <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 within a class</li> <li>Supports multi-sensor and multi-temporal data processing through in-house SMIC software</li> <li>Includes case studies and practical applications for single class mapping</li> </ul><p>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.</p>