Deep Learning Classifiers Hyperspectral by Kanthi Murali (5 results)

Taschenbuch. Condition: 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.

Taschenbuch. Condition: 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.

Condition: 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.

Taschenbuch. Condition: 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.

Taschenbuch. Condition: 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.