Dual-Path Network-Based Hyperspectral Image Classification

Recently, convolutional neural networks (CNNs) as a powerful tool have been introduced for classification of hyperspectral images (HSIs). However, it fails to take the feature redundancy into consideration. Hence, for the pixel-wise HSI classification, the CNN-based methods may not effectively extract the discriminative features from the complex scene in HSIs. In order to overcome this problem, in this letter, a novel dual-path network (DPN)-based HSI classification method is proposed, in which the DPN combines the advantages of the residual network and dense convolutional network. First, the principal component analysis is utilized to extract significant components of HSI. Second, training image patches centered on labeled pixels are constructed to train the DPN. Finally, the labels of test pixels are predicted by using the trained network. Experiments conducted on two hyperspectral data sets demonstrate the state-of-the-art performance of the proposed method over other compared methods in terms of classification accuracies.