Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for
  Hyperspectral Image Clustering
Renxiang Guan Zihao Li Xianju Li Chang Tang
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 super pixel 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 super pixel-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 super
pixel contrastive learning and pseudo-label correction (PSCPC) method for the
HSI clustering. PSCPC can reasonably capture domain-specific and fine-grained
features through super pixels and the comparative learning of a small number of
pixels within the super pixels. To improve the clustering performance of super
pixels, this paper proposes a pseudo-label correction module that aligns the
clustering pseudo-labels of pixels and super-pixels. In addition, pixel-level
clustering results are used to supervise super pixel-level clustering,
improving the generalization ability of the model. Extensive experiments
demonstrate the effectiveness and efficiency of PSCPC.