  Hyperspectral images (HSI) clustering is an important but challenging task.
The state-of-the-art (SOTA) methods usually rely on superpixels, however, they
do not fully utilize the spatial and spectral information in HSI 3-D structure,
and their optimization targets are not clustering-oriented. In this work, we
first use 3-D and 2-D hybrid convolutional neural networks to extract the
high-order spatial and spectral features of HSI through pre-training, and then
design a superpixel graph contrastive clustering (SPGCC) model to learn
discriminative superpixel representations. Reasonable augmented views are
crucial for contrastive clustering, and conventional contrastive learning may
hurt the cluster structure since different samples are pushed away in the
embedding space even if they belong to the same class. In SPGCC, we design two
semantic-invariant data augmentations for HSI superpixels: pixel sampling
augmentation and model weight augmentation. Then sample-level alignment and
clustering-center-level contrast are performed for better intra-class
similarity and inter-class dissimilarity of superpixel embeddings. We perform
clustering and network optimization alternatively. Experimental results on
several HSI datasets verify the advantages of the proposed method, e.g., on
India Pines, our model improves the clustering accuracy from 58.79% to 67.59%
compared to the SOTA method.
