SDST: Self-Supervised Double-Structure Transformer for Hyperspectral Images Clustering

Due to the lack of labeled information and the high spectral variability in high-dimensional hyperspectral images (HSI), HSI clustering has emerged as an effective unsupervised approach for HSI information extraction and classification. Deep clustering methods have achieved significant success in unsupervised HSI classification (HSIC) and have gained increasing attention. However, these methods have limitations in terms of robustness, adaptability, and feature representation when dealing with complex large-scale HSI datasets. Therefore, this paper introduced a novel Self-supervised Double-Structure Transformer (SDST) approach for hyperspectral image clustering. Specifically, in our approach, we designed a shared Autoformer structure based on autoencoder to learn the global properties of HSI data by fusing the multi-level features from autoencoder with Transformer. Furthermore, we proposed a siamese Dual-Former Graph Module with superpixel-level features for fewer nodes, which reveals long-dependency graph convolutional features, resulting in more precise graph structure features. By constructing graph with long-dependencies, this module significantly preserves the properties of global dependencies, while focusing on the local features of each superpixel to better represent the fine-grained local details. Finally, we designed a Joint Optimization Module to jointly optimize the double-structure model composed of the shared Autoformer Module and the siamese Dual-Former Graph Module. To validate the effectiveness of the proposed SDST method, we conducted a series of experiments on the Salinas, Botswana, Indian Pines, and Houston2013 datasets. The proposed SDST achieves competitive clustering accuracies compared with the state-of-the-art clustering methods. Codes: https://github.com/YiLiu1999/SDST.