Deep Learning Classifiers for Hyperspectral Image Analysis - Kanthi, Murali;Sarma, T. Hitendra;Bindu, C. Shoba
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
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.