Hyperspectral Image Classification Via Double-Branch Multi-Scale Spectral-Spatial Convolution Network

Since traditional convolutional neural network (CNN) is used to extract the spectral-spatial features of hyperspectral image (HSI) will result in lots of spatial information redundancy. Octave convolution is used to replace the traditional CNN to reduce spatial redundancy and expand the receptive field. However, the methods based on 3D octave convolution may cause many parameters and the model to be complicated. To address these issues, we propose an HSI classification approach based on a double-branch multi-scale spectral-spatial convolution network (DBMS) in this paper. Firstly, We utilize 2D octave convolution and 3D DenseNet sub-networks with different kernels sizes to extract complex spatial features and spectral features, respectively. Furthermore, a channel attention module and a spectral attention module are employed in this two sub-network respectively, to highlight the important feature areas and specific spectral bands that consist of significant information for the classification. Compared with several other state-of-the-art methods, our proposed method can achieve competitive performance on Salinas Valley (SV) HSI dataset.