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Fuzzy machine learning algorithms for remote sensing image classification / Anil Kumar, A. Senthil Kumar, Priyadarshi Upadhyay.

By: Kumar, Anil (Engineer) [author.].
Contributor(s): Kumar, A. Senthil [author.] | Upadhyay, Priyadarshi [author.].
Material type: materialTypeLabelBookPublisher: Boca Raton : CRC Press, 2020Edition: First edition.Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780429340369; 0429340362; 9781000091540; 1000091546; 9781000091526; 100009152X; 9781000091533; 1000091538.Subject(s): Remote-sensing images -- Classification | Remote-sensing images -- Data processing | Machine learning | Fuzzy algorithms | COMPUTERS / Machine Theory | TECHNOLOGY / Environmental Engineering & Technology | TECHNOLOGY / Imaging SystemsDDC classification: 526.9/820285633 Online resources: Taylor & Francis | OCLC metadata license agreement
Contents:
Machine learning -- Ground truth data for remote sensing image classification -- Fuzzy classifiers -- Learning based classifiers -- Hybrid fuzzy classifiers -- Fuzzy classifiers for temporal data processing -- Assessment of accuracy for soft classification.
Summary: "This book covers the state of art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy based learning methods including applications for preparing land cover classification outputs from actual satellite data. It provides details about the temporal indices database using proposed class-based sensor independent approach supported by practical examples. Fuzzy based algorithms with machine learning algorithms to prepare land cover maps is discussed. Accuracy assessment for soft classification outputs are included and all algorithms are supported by in-house developed tool as Sub-pixel Multi-spectral Image Classifier"-- Provided by publisher.
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Machine learning -- Ground truth data for remote sensing image classification -- Fuzzy classifiers -- Learning based classifiers -- Hybrid fuzzy classifiers -- Fuzzy classifiers for temporal data processing -- Assessment of accuracy for soft classification.

"This book covers the state of art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy based learning methods including applications for preparing land cover classification outputs from actual satellite data. It provides details about the temporal indices database using proposed class-based sensor independent approach supported by practical examples. Fuzzy based algorithms with machine learning algorithms to prepare land cover maps is discussed. Accuracy assessment for soft classification outputs are included and all algorithms are supported by in-house developed tool as Sub-pixel Multi-spectral Image Classifier"-- Provided by publisher.

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