Masked Superpixel Contrastive Subspace Clustering Network for Unsupervised Large-Scale Hyperspectral Image Classification

Subspace clustering contributes a lot to the development of unsupervised hyperspectral image (HSI) classification task due to its ability of processing high-dimensional data. Generally, subspace clustering suffers from the bottlenecks of high computational cost when dealing with large-scale HSI. Some researches address this problem by using superpixel segmentation. However, the introduction of superpixel segmentation may cause some adverse effects, including interference from non-target region samples and the oversmoothing of superpixel-level samples. To overcome these issues, we propose a masked superpixel contrastive subspace clustering (MSCSC) for large-scale HSI classification. Specifically, we leverage hyperspectral masked autoencoder rather than conventional autoencoder as the backbone to mitigate the interference of non-target region samples during feature extraction. Then, based on this backbone, we integrate contrastive learning with self-expressiveness based subspace clustering to form the superpixel-level contrastive subspace clustering network, which aims to learn the discriminative superpixel-level self-representation. Moreover, we design a novel data augmentation strategy to ensure learn global and local information, improving the distinguishability of superpixel-level features. Experiments on four popular HSI datasets validate the superiority of our proposed method, with a large accuracy improvement compared to state-of-the-art methods.