Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering

Renxiang Guan, Zihao Li, Xianju Li, Chang Tang

Abstract: Hyperspectral image (HSI) clustering is gaining considerable attention owing to recent methods that overcome the inefficiency and misleading results from the absence of supervised information. Contrastive learning methods excel at existing pixel-level and superpixel-level HSI clustering tasks. The pixel-level contrastive learning method can effectively improve the ability of the model to capture fine features of HSI but requires a large time overhead. The superpixel-level contrastive learning method utilizes the homogeneity of HSI and reduces computing resources; however, it yields rough classification results. To exploit the strengths of both methods, we present a pixel–superpixel contrastive learning and pseudo-label correction (PSCPC) method for the HSI clustering. PSCPC can reasonably capture domain-specific and fine-grained features through superpixels and the comparative learning of a small number of pixels within the superpixels. To improve the clustering performance of superpixels, this paper proposes a pseudo-label correction module that aligns the clustering pseudo-labels of pixels and superpixels. In addition, pixel-level clustering results are used to supervise superpixel-level clustering, improving the generalization ability of the model. Extensive experiments demonstrate the effectiveness and efficiency of PSCPC.