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Hyperspectral image classification is the most popular research area in the hyperspectral community and has attracted significant interest in remote sensing. HSI classification is a challenging task because of the large dimensionality of the data, inadequate datasets, huge data, and limited training samples. Several Deep Learning (DL) based architectures are being explored to resolve the aforementioned challenges and provide significant improvements in HSI data analysis. Limited studies have been presented in the literature in the direction of exploring deep learning architectures for joint spatial and spectral features to achieve high accuracy of pixel classification. This book presents different deep-learning approaches for efficient spatial-spectral features for the classification of pixels in HSI images.
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Deep Learning Classifiers for Hyperspectral Image Analysis
Editore:
LAP LAMBERT Academic Publishing Nov 2022, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Hyperspectral image classification is the most popular research area in the hyperspectral community and has attracted significant interest in remote sensing. HSI classification is a challenging task because of the large dimensionality of the data, inadequate datasets, huge data, and limited training samples. Several Deep Learning (DL) based architectures are being explored to resolve the aforementioned challenges and provide significant improvements in HSI data analysis. Limited studies have been presented in the literature in the direction of exploring deep learning architectures for joint spatial and spectral features to achieve high accuracy of pixel classification. This book presents different deep-learning approaches for efficient spatial-spectral features for the classification of pixels in HSI images. 152 pp. Englisch. Codice articolo 9786205514139
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Deep Learning Classifiers for Hyperspectral Image Analysis
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LAP Lambert Academic Publishing, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Hyperspectral image classification is the most popular research area in the hyperspectral community and has attracted significant interest in remote sensing. HSI classification is a challenging task because of the large dimensionality of the data, inadequat. Codice articolo 769216656
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Deep Learning Classifiers for Hyperspectral Image Analysis
Editore:
LAP LAMBERT Academic Publishing Nov 2022, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
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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 image classification is the most popular research area in the hyperspectral community and has attracted significant interest in remote sensing. HSI classification is a challenging task because of the large dimensionality of the data, inadequate datasets, huge data, and limited training samples. Several Deep Learning (DL) based architectures are being explored to resolve the aforementioned challenges and provide significant improvements in HSI data analysis. Limited studies have been presented in the literature in the direction of exploring deep learning architectures for joint spatial and spectral features to achieve high accuracy of pixel classification. This book presents different deep-learning approaches for efficient spatial-spectral features for the classification of pixels in HSI images.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch. Codice articolo 9786205514139
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Deep Learning Classifiers for Hyperspectral Image Analysis
Editore:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Hyperspectral image classification is the most popular research area in the hyperspectral community and has attracted significant interest in remote sensing. HSI classification is a challenging task because of the large dimensionality of the data, inadequate datasets, huge data, and limited training samples. Several Deep Learning (DL) based architectures are being explored to resolve the aforementioned challenges and provide significant improvements in HSI data analysis. Limited studies have been presented in the literature in the direction of exploring deep learning architectures for joint spatial and spectral features to achieve high accuracy of pixel classification. This book presents different deep-learning approaches for efficient spatial-spectral features for the classification of pixels in HSI images. Codice articolo 9786205514139
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