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

• Main Authors: Kumar, Anil, Upadhyay, Priyadarshi, Singh, Uttara
• Format: eBook
• Language: English
• Published: Milton CRC Press 2023; Taylor & Francis Group
• Edition: 1
• Subjects: Remote sensing; TVI; Class Mapping; Machine Learning; TOA Reflectance; Hyper-spectral Data; Membership Values; Multi-Temporal Remote Sensing; Hyperspectral Images; Convolution Layer; CASI Data; Spectral Indices; TOA Radiance; Hyperspectral Data; CNN Model; PCA Component; Temporal Data; Training Samples; Fuzzy Logic; Red Edge Band; Spectral Index; Hyperspectral Remote Sensing; Reset Gate; October Image; Temporal Vegetation Indices; NDWI; Remote Sensing Images; Algorithms; NC; Hyper-spectral Images; Vegetation Indices; Triangular Vegetation Index
• ISBN: 1032446528; 9781032428321; 1032428325; 9781032446523
• DOI: 10.1201/9781003373216

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.