Hyperspectral images classification with hybrid deep residual network

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved effective performance. In general, the previous networks are not enough deep, which might not extract very discriminant features for classification. In addition, they do not consider strong correlations among different hierarchical layers. Due to the two problems, a hybrid deep residual network is presented for HSIs classification in this paper. The proposed method firstly employs deep residual network (DRN) to extract very deep and discriminant features of HSIs. The DRN can help to overcome the decrease of classification accuracy that is caused by the increasing network depth and limited available training samples. Moreover, by incorporating different hierarchical features of network with a hybrid mechanism, the classification results can be further improved. Experimental results on a real hyperspectral image demonstrate that the proposed method outperforms other competitive methods.