Multiscale Densely-Connected Fusion Networks for Hyperspectral Images Classification

Convolutional neural network (CNN) has demonstrated to be a powerful tool for hyperspectral images (HSIs) classification. Previous CNN-based HSI classification methods only adopt the fixed-size patches to train the CNN model, and such single scale patches may not reflect the complex spatial structural information in the HSIs. In addition, although different layers of CNN can extract features of multiple scales, the traditional CNN model can only utilize features from the highest level for the classification task. These features, however, do not fully consider the strong complementary yet correlated information among different layers. To address these issues, in this paper, a multiscale densely-connected convolutional network (MS-DenseNet) framework is proposed to sufficiently exploit multiple scales information for the HSIs classification. Specifically, for each pixel, the MS-DenseNet, first, extracts its surrounding patches of multiple scales. These patches can separately constitute multiple scale training and testing samples. Within each specific scale sample, instead of using the forward convolutional layers, the MS-DenseNet adopts the dense blocks, which can connect each layer to other layers in a feed-forward fashion and thus can exploit the information among different layers for training and testing. Furthermore, since high correlations exist in patches of different scales, the MS-DenseNet introduces several dense blocks to fuse the multiscale information among different layers for the final HSI classification. Experimental results on several real HSIs demonstrate the superiority of the proposed MS-DenseNet over single scale-based CNN classification model and several well-known classification methods.