Hyperspectral Remote Sensing Images Classification Using a Deep Convolutional Neural Network Model

The traditional hyperspectral image classification model only considers the spectral feature information, and ignores the important role of image spatial structure information in classification. In order to improve the classification accuracy of hyperspectral remote sensing image, this paper present a deep learning model utilizing the rich spectral and spatial information in hyperspectral images for land cover classification application. The proposed model is able to automatically extract more abstract high-level features from the low-level features for classification. In addition, the network structure is highly invariant to translation, scaling and other forms of distortion. Experiment results show that the deep learning method can provide high performances in hyperspectral image classification applications.The feasibility and effectiveness of the deep convolution neural network for classification of hyperspectral images are verified.