Hyperspectral Image Classification Based on Multiscale Spatial and Spectral Feature Network

With the development of deep learning, hyperspectral image (HSI) classification tasks have developed rapidly, the classification performance is improved in a big degree. Despite the great success of the existing methods, there is still room for improvement to extract features from spatial and spectral dimensions. In this paper, we propose a multiscale spatial and spectral feature network (MSSFN) to capture discriminative features for the classification of HSIs. Specifically, we first use three convolution layers to extract the features of original HSI data. Second, combining the spatial masks model and spectral attention model to build multiscale spatial and spectral model (MSSM). Through the MSSM model, the spatial information of different scales can be obtained and the useful spectral bands can be emphasized. Finally, in order to reduce the computation complexity and simplify network, the other three convolution layers with a small number of convolution kernels are adopted in our method. The experimental results demonstrate that our method is superior to most existing methods on two public HSI datasets.