Classification of hyperspectral images using a propagation filter and convolutional neural network

Deep Learning models have been successfully applied to the classification of hyperspectral images (HSIs), among which Convolutional Neural Network (CNN) can extract more efficient spatial-spectral features to improve classification accuracy compared to traditional classifiers such as Support Vector Machine. However, in the case of limited labelled samples with inevitable noise, simply applying CNN for HSIs classification could be unsatisfactory due to the issue of overfitting. To address the problem, data preprocessing techniques such as filtering methods can be used to effectively remove the noise and enhance the spatial-spectral features. Compared to typical filters like Gabor, Propagation Filter (PF) can effectively smooth the HSIs and preserve the edges without causing the cross-region mixing problem, which could significantly compromise the performance of CNN based HSIs classification models. Therefore, in this paper a new model which combine Propagation Filter with CNN (PF-CNN) is proposed. It makes use of the advantages of PF and CNN to effectively handle the cross-region mixing issue for HSIs classification. The experimental results show that the proposed algorithm outperforms other state-of-the-art HSIs classification models. The source code of the proposed model is available at https://github.com/Xinweiiiang/Propagation-Filter-CNN.