SSUFormer: Spatial-spectral UnetFormer for improving hyperspectral image classification☆,☆☆

초록

For hyperspectral image (HSI) classification, convolutional neural networks with a local kernel neglect the global HSI properties, and transformer networks often predict only the central pixel. This study proposes a spatial-spectral UnetFormer network to extract the full local and global spatial similarities and the long shortrange spectral dependencies for HSI classification. This approach fuses a spectral transformer subnetwork and a spatial attention U-net subnetwork to create outputs. In the spectral subnetwork, the transformer is tailored at the embedding and head layers to generate a prediction for all input pixels. In the spatial attention U-net subnetwork, a local-global spatial feature model is introduced based on the U-net structure with a singular value decomposition-aided spatial self-attention module to emphasize useful details, mitigate the impact of noise, and eventually learn the global spatial features. The proposed model obtains competitive results with state-of-the-art methods in HSI classification on various public datasets.

키워드