Inhaltsangabe
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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Über die Autorin bzw. den Autor
Dr. Murali Kanthi, received Ph.D in CSE from JNTUA, Anantapuramu, Andhra Pradesh, India. He is currently working as an Associate Professor in the Department of CSE, CMR Technical Campus, Hyderabad, Telangana, India. His research areas include Data Mining, Machine Learning, Deep Learning, and Hyperspectral Image Processing.
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Deep Learning Classifiers for Hyperspectral Image Analysis
Verlag:
LAP LAMBERT Academic Publishing Nov 2022, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
Neu
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786205514139
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Deep Learning Classifiers for Hyperspectral Image Analysis
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ISBN 10: 6205514133
ISBN 13: 9786205514139
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Zustand: 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. Bestandsnummer des Verkäufers 769216656
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Deep Learning Classifiers for Hyperspectral Image Analysis
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
Neu
Taschenbuch
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Taschenbuch. Zustand: Neu. Deep Learning Classifiers for Hyperspectral Image Analysis | Murali Kanthi (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205514139 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 125976809
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Deep Learning Classifiers for Hyperspectral Image Analysis
Verlag:
LAP LAMBERT Academic Publishing Nov 2022, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
Neu
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786205514139
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Deep Learning Classifiers for Hyperspectral Image Analysis
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205514133
ISBN 13: 9786205514139
Neu
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786205514139
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