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- Toujours un magasin près de chez vous
Multi-Sensor and Multi-Temporal Remote Sensing
Specific Single Class Mapping
Anil Kumar, Priyadarshi Upadhyay, Uttara Singh
Livre relié | Anglais
105,45 €
+ 210 points
Description
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.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 148
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9781032428321
- Date de parution :
- 17-04-23
- Format:
- Livre relié
- Format numérique:
- Genaaid
- Dimensions :
- 156 mm x 234 mm
- Poids :
- 430 g
