ISBN: 9781032428321 - multi-sensor and multi-temporal remote sensing: specific single class mapping (16 Ergebnisse)

Hardcover. Zustand: new. Hardcover. 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 classesDiscusses range of fuzzy/deep learning models capable to extract specific single class and separates noiseDescribes 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 classSupports multi-sensor and multi-temporal data processing through in-house SMIC softwareIncludes case studies and practical applications for single class mappingThis 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. This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, fuzzy machine learning models supported by case studies. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Hardcover. Zustand: new. Hardcover. 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 classesDiscusses range of fuzzy/deep learning models capable to extract specific single class and separates noiseDescribes 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 classSupports multi-sensor and multi-temporal data processing through in-house SMIC softwareIncludes case studies and practical applications for single class mappingThis 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. This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, fuzzy machine learning models supported by case studies. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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Hardcover. Zustand: Brand New. 184 pages. 9.19x6.13x0.59 inches. In Stock.

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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, fuzzy machine learning models supported by case studies.…