Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification

Deep convolutional neural networks (CNNs) have brought in achievements in image classification and target detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normalization, dropout, Parametric Rectified Linear Unit (PReLu) activation function. By taking advantage of the specific characteristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Experimental results demonstrate that our proposed CNN out- performs the state-of-the-art methods.