Sinossi
Hyperspectral Images (HSIs) are popular in diversified applications, such as; Geo-sciences, Biomedical imaging, Agriculture, and physics-related research. The rich spatial and spectral information of HSI are the key factors for robust representation of class-specific objects, in remote sensing applications. But these images often suffer from the Hughes effect. This demands a dimensionality reduction using feature selection. The feature selection process is commonly called Band Selection (BS) for the HS dataset. This Book is mainly focused on three proposed models, where, mostly the clustering based unsupervised strategies are adopted for BS. First, Derivative-based band clustering and multi-agent PSO optimization for optimal band selection (DBC_MAPSO) is proposed. But they are time consuming and the selected bands are not persistent for each evaluation, due to the random nature of the optimizers. To overcome this, Spatial residual clustering and entropy-based ranking (SRC_EBR) and Featured clustering and ranking based bad cluster removal (FC_RBCR) are proposed.
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Clustering Based Band Selection
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LAP LAMBERT Academic Publishing Jun 2022, 2022
ISBN 10: 6204957244
ISBN 13: 9786204957241
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Hyperspectral Images (HSIs) are popular in diversified applications, such as; Geo-sciences, Biomedical imaging, Agriculture, and physics-related research. The rich spatial and spectral information of HSI are the key factors for robust representation of class-specific objects, in remote sensing applications. But these images often suffer from the Hughes effect. This demands a dimensionality reduction using feature selection. The feature selection process is commonly called Band Selection (BS) for the HS dataset. This Book is mainly focused on three proposed models, where, mostly the clustering based unsupervised strategies are adopted for BS. First, Derivative-based band clustering and multi-agent PSO optimization for optimal band selection (DBC_MAPSO) is proposed. But they are time consuming and the selected bands are not persistent for each evaluation, due to the random nature of the optimizers. To overcome this, Spatial residual clustering and entropy-based ranking (SRC_EBR) and Featured clustering and ranking based bad cluster removal (FC_RBCR) are proposed. 128 pp. Englisch. Codice articolo 9786204957241
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Clustering Based Band Selection
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ISBN 13: 9786204957241
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Clustering Based Band Selection
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LAP LAMBERT Academic Publishing Jun 2022, 2022
ISBN 10: 6204957244
ISBN 13: 9786204957241
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Hyperspectral Images (HSIs) are popular in diversified applications, such as; Geo-sciences, Biomedical imaging, Agriculture, and physics-related research. The rich spatial and spectral information of HSI are the key factors for robust representation of class-specific objects, in remote sensing applications. But these images often suffer from the Hughes effect. This demands a dimensionality reduction using feature selection. The feature selection process is commonly called Band Selection (BS) for the HS dataset. This Book is mainly focused on three proposed models, where, mostly the clustering based unsupervised strategies are adopted for BS. First, Derivative-based band clustering and multi-agent PSO optimization for optimal band selection (DBC_MAPSO) is proposed. But they are time consuming and the selected bands are not persistent for each evaluation, due to the random nature of the optimizers. To overcome this, Spatial residual clustering and entropy-based ranking (SRC_EBR) and Featured clustering and ranking based bad cluster removal (FC_RBCR) are proposed.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 128 pp. Englisch. Codice articolo 9786204957241
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Clustering Based Band Selection | Classification of Hyperspectral Images
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204957244
ISBN 13: 9786204957241
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Taschenbuch. Condizione: Neu. Clustering Based Band Selection | Classification of Hyperspectral Images | Kishore Raju Kalidindi (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204957241 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 122077532
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Clustering Based Band Selection : Classification of Hyperspectral Images
Editore:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204957244
ISBN 13: 9786204957241
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Hyperspectral Images (HSIs) are popular in diversified applications, such as; Geo-sciences, Biomedical imaging, Agriculture, and physics-related research. The rich spatial and spectral information of HSI are the key factors for robust representation of class-specific objects, in remote sensing applications. But these images often suffer from the Hughes effect. This demands a dimensionality reduction using feature selection. The feature selection process is commonly called Band Selection (BS) for the HS dataset. This Book is mainly focused on three proposed models, where, mostly the clustering based unsupervised strategies are adopted for BS. First, Derivative-based band clustering and multi-agent PSO optimization for optimal band selection (DBC_MAPSO) is proposed. But they are time consuming and the selected bands are not persistent for each evaluation, due to the random nature of the optimizers. To overcome this, Spatial residual clustering and entropy-based ranking (SRC_EBR) and Featured clustering and ranking based bad cluster removal (FC_RBCR) are proposed. Codice articolo 9786204957241
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