Multiscale Spectral-Spatial Unified Networks For Hyperspectral Image Classification

Abstract

The combination of the spectral and spatial features is received wide attention in hyperspectral image (HSI) classification. And the multiscale-strategy is an effective way in improving the classification accuracy for HSI due to the various sizes of land covers, which can capture more intrinsic information. For this reason, a multiscale spectral-spatial unified network (MSSN) with two-branch architecture is proposed for hyperspectral image classification. Different from other networks mainly focusing on the multiscale spatial features, the MSSN can jointly extract the multiscale spectral-spatial features, which is based on the reason that features of different layers in CNN correspond to different scales. In the implementation of the MSSN, the 1D CNN and 2D CNN are used to extract the spectral and spatial features respectively. Then the features of the corresponding layers in the two branches will be integrated to the fully-connected layers and finally sent to the classification layers. Experiments on two benchmark HSIs demonstrate that the proposed MSSN can yield a competitive performance compared with other existing methods.