The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …

Every pixel in a hyperspectral image contains detailed spectral information in hundreds of
narrow bands captured by hyperspectral sensors. Pixel-wise classification of a hyperspectral …

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper
and wider than other existing deep networks for hyperspectral image classification. Unlike …

Deep learning has achieved great successes in conventional computer vision tasks. In this
paper, we exploit deep learning techniques to address the hyperspectral image …

In recent years, deep learning based methods have attracted broad attention in the field of
hyperspectral image classification. However, due to the massive parameters and the …

As a powerful visual model, convolutional neural networks (CNNs) have demonstrated
remarkable performance in various visual recognition problems, and attracted considerable …

In recent years, image classification on hyperspectral imagery utilizing deep learning
algorithms has attained good results. Thus, spurred by that finding and to further improve the …

Nowadays, deep learning methods, especially the convolutional neural networks (CNNs),
have shown impressive performance on extracting abstract and high-level features from the …

Convolutional neural networks (CNNs) have been widely used for hyperspectral image
classification. As a common process, small cubes are first cropped from the hyperspectral …

Recently, classification and dimensionality reduction (DR) have become important issues of
hyperspectral image (HSI) analysis. Especially, HSI classification is a challenging task due …