Geologic Body Classification of Hyperspectral Data Based on Dilated Convolution Neural Network at Tianshan Area

Hyperspectral data contains abundant information in spectral domain, which is very useful for mineral classification and geological body mapping. But, due to the lack of labeled data, it is difficult to get an acceptable result by just using the small number of labeled data. We adopt a semi-supervised method called CNN, which can effectively extract inner features of hyperspectral image to classify hyperspectral data. However, with constraint to the size of receptive field, it can hardly get higher level features. We propose dilated CNN for mineral classification of hyperspectral data. At the same size of kernels, dilated CNN has bigger receptive field. At the meanwhile, it can get higher accuracy of classification. We test our model on hyperspectral data at Tianshan area, where is rich in minerals. From the result, we can find that our method can get a great result on the mineral classification task, which can be used for making geological map.