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Multi-Sensor and Multi-Temporal Remote Sensing : Specific Single Class Mapping
- Publication Year :
- 2023
-
Abstract
- 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.
- Subjects :
- Remote sensing
Subjects
Details
- Language :
- English
- ISBNs :
- 9781032428321, 9781032446523, 9781000872194, 9781000872200, and 9781003373216
- Database :
- eBook Index
- Journal :
- Multi-Sensor and Multi-Temporal Remote Sensing : Specific Single Class Mapping
- Publication Type :
- eBook
- Accession number :
- 3587696