Clustering Based Band Selection: Classification of Hyperspectral Images - Couverture souple

Kalidindi, Kishore Raju; G. P., Saradhi Varma; D., Rajyalakshmi

9786204957241: Clustering Based Band Selection: Classification of Hyperspectral Images

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ISBN 10 : 6204957244 ISBN 13 : 9786204957241

Editeur : LAP LAMBERT Academic Publishing, 2022

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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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2022
Langue: anglais
ISBN 10: 6204957244
ISBN 13: 9786204957241
Reliure: Broch�
Nombre de pages: 128
Coordonn�es du fabricant: non disponible
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Clustering Based Band Selection

Kishore Raju Kalidindi|Saradhi Varma G. P.|Rajyalakshmi D.

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ISBN 10 : 6204957244 ISBN 13 : 9786204957241

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Clustering Based Band Selection

Kishore Raju Kalidindi

Edit� par LAP LAMBERT Academic Publishing Jun 2022, 2022

ISBN 10 : 6204957244 ISBN 13 : 9786204957241

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9786204957241

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Clustering Based Band Selection : Classification of Hyperspectral Images

Kishore Raju Kalidindi

Edit� par LAP LAMBERT Academic Publishing, 2022

ISBN 10 : 6204957244 ISBN 13 : 9786204957241

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9786204957241

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Clustering Based Band Selection

Kishore Raju Kalidindi

Edit� par LAP LAMBERT Academic Publishing Jun 2022, 2022

ISBN 10 : 6204957244 ISBN 13 : 9786204957241

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. 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.Books on Demand GmbH, �berseering 33, 22297 Hamburg 128 pp. Englisch. N� de r�f. du vendeur 9786204957241

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