Multi-Sensor and Multi-Temporal Remote Sensing: Specific Single Class Mapping, 1st Edition eBook
$30.00
. 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.
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About this eTextbook
. 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.
ISBNs for Multi-Sensor and Multi-Temporal Remote Sensing are 9781000872200, 1000872203 and the print ISBNs are 9781032428321, 1032428325. Additional ISBNs include 9781003373216, 9781032446523, 9781000872194.
Publication details
- Author: Anil Kumar; Priyadarshi Upadhyay; Uttara Singh
- Publisher: CRC Press
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About this eTextbook
. 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.
ISBNs for Multi-Sensor and Multi-Temporal Remote Sensing are 9781000872200, 1000872203 and the print ISBNs are 9781032428321, 1032428325. Additional ISBNs include 9781003373216, 9781032446523, 9781000872194.
Publication details
- Author: Anil Kumar; Priyadarshi Upadhyay; Uttara Singh
- Publisher: CRC Press
What you receive
- A licensed digital edition of this title, supplied as PDF or EPUB. The format is set by the publisher, so email us the ISBN before ordering and we will confirm which one it is.
- Delivered to the email address on your order as a secure download link, usually within minutes and always within 24 hours of payment.
- Download links do not expire and there is no cap on re-downloads. Readable on any computer, tablet or phone, online or offline.
- This is a digital edition. Nothing is printed, packed or posted, and there is no delivery charge.
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