HFC-SST: improved spatial-spectral transformer for hyperspectral few-shot classification

作者：Zhiquan Huang, Haojin Tang, Yanshan Li, Weixin Xie · 发表于：Journal of Applied Remote Sensing · 年份：2023 · DOI：10.1117/1.jrs.17.026509 · 被引用次数：11 · 研究领域：Remote-Sensing Image Classification、Remote Sensing and Land Use、Advanced Image Fusion Techniques

Owing to the complex environment of hyperspectral image (HSI) collecting area, it is difficult to obtain an extensive number of labeled samples for HSI. Recently, many few-shot learning (FSL) algorithms based on convolutional neural network (CNN) have been employed for HSI classification in the scenery of small-scale training samples. However, a CNN-based model is unsuitable for modeling the spatial-spectral information with long-range dependency. The transformer has proved its superiority in modeling the long-range dependency. Inspired by this, an improved spatial-spectral transformer for HSI few-shot classification (HFC-SST) is proposed to deeply extract the local spatial-spectral information with only a few labeled samples. The contribution of this letter is twofold. First, a local spatial-spectral sequence generation method based on spatial-spectral correlation analysis and adjacent position information is proposed to generate the input sequence for transformer. Second, a local spatial-spectral feature extraction network based on the transformer is proposed to further exploit the spatial-spectral feature information on the input sequence. Experimental results on HFC with four datasets confirm that our proposed HFC-SST algorithm can achieve higher classification accuracy than the traditional CNN algorithms and the HSI FSL algorithms.