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

di Anil Kumar (Autore)

Priyadarshi Upadhyay (Autore)

Uttara Singh (Autore)

Taylor & Francis Ltd, 2025

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.

• Autore: Anil Kumar, Priyadarshi Upadhyay, Uttara Singh
• Editore: Taylor & Francis Ltd
• Anno: 2025
• Rilegatura: Paperback / softback
• Pagine: 148 p.
• Dimensioni: 234 x 156 mm
• Peso: 330 gr.
• EAN: 9781032446523

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