Patch-Free Bilateral Network for Hyperspectral Image Classification Using Limited Samples

Recently, data-driven methods represented by deep learning have been widely used in hyperspectral image (HSI) classification and achieved the promising success. However, using less labeled samples to obtain higher classification accuracy is still a challenging task. In this study, we propose a patch-free bilateral network (PBiNet) for HSI classification. In order to make better use of the features with different scales, PBiNet uses the spatial path and the semantic path to obtain different level features for classification. The spatial path with small stride is used to retain the spatial detail information. The semantic path with fast down sampling rate is used to retain high-level semantic information. Using fast downsampling rates is to expand the scope of receptive field, so that semantic branch can better focus on global information. Then we design a feature fusion module to fuse the features obtained by the two paths. Finally, we use the classification maps produced by different scale features to calculate the loss function to optimize the whole model. Due to the better use of different levels of features, the proposed method could achieve higher classification accuracy with limited labeled samples. More importantly, because the whole HSI is used as the input, the proposed method has higher computational efficiency. To verify the effectiveness of the proposed method, we carried out classification experiments on four popular HSI datasets. Quantitative and qualitative experimental results show that the accuracy of the proposed method exceeds the compared methods.