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.

翻译：高光谱图像聚类因近期克服了无监督信息导致的低效与误导性结果的方法而受到广泛关注。对比学习方法在现有像素级与超像素级高光谱聚类任务中表现出色。像素级对比学习方法能有效提升模型捕获高光谱精细特征的能力，但需要大量时间开销。超像素级对比学习方法利用高光谱图像的均匀性并降低计算资源消耗，但会产生粗糙的分类结果。为融合两种方法的优势，我们提出一种面向高光谱图像聚类的像素-超像素对比学习与伪标签校正（PSCPC）方法。PSCPC通过超像素及其内部少量像素的对比学习，能够合理捕获领域特定与细粒度特征。为提升超像素聚类性能，本文提出一种伪标签校正模块，用于对齐像素与超像素的聚类伪标签。此外，利用像素级聚类结果监督超像素级聚类，从而增强模型的泛化能力。大量实验证明了PSCPC的有效性与高效性。