| Summary: | ABSTRACTIn recent years, the convolutional neural network (CNN) has been widely applied to hyperspectral image classification because of its powerful feature capture ability. Nevertheless, the performance of most convolutional operations is limited by the fixed shape and size of the convolutional kernel, which causes CNN cannot fully extract global features. To address this issue, this article proposes a novel classification architecture named spatial-spectral hierarchical Vision Permutator (S2HViP). It contains a spectral module and a spatial module. In the spectral module, we divide the data into groups along the spectral dimension and treat each pixel within the group as a spectral token. Spectral long-range dependencies are obtained by fusing intra- and inter-group spectral correlations captured by multi-layer perceptrons (MLPs). In the spatial module, we first model spatial information via morphological methods and divide the resulting spatial feature maps into spatial tokens of uniform size. Then, the global spatial information is extracted through MLPs. Finally, the extracted spectral and spatial features are combined for classification. Particularly, the proposed MLP structure is an improved Vision Permutator, which presents a hierarchical fusion strategy aiming at generating discriminative features. Experimental results show that S2HViP can provide competitive performance compared to several state-of-the-art methods.
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