TY  - BOOK
AU  - Kumar,Anil
AU  - Upadhyay,Priyadarshi
AU  - Singh,Uttara
TI  - Multi-sensor and multi-temporal remote sensing: specific single class mapping
SN  - 9781003373216
AV  - G70.4 
U1  - 621.3678 23/eng/20230501
PY  - 2023///
CY  - [Place of publication not identified]
PB  - CRC Press
KW  - Remote sensing
KW  - COMPUTERS / Machine Theory
KW  - bisacsh
KW  - TECHNOLOGY / Environmental Engineering & Technology
KW  - TECHNOLOGY / Remote Sensing
N2  - 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
UR  - https://www.taylorfrancis.com/books/9781003373216
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -