SLCGC：一种用于高光谱图像的轻量级自监督低通对比图聚类网络
SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images
AI总结：
本文提出轻量级自监督低通对比图聚类网络SLCGC，通过同质区域生成、低通图去噪和双分支图对比学习提升高光谱图像聚类的精度、效率与抗噪性。
摘要：
自监督高光谱图像（HSI）聚类由于缺乏标注数据以及空间-光谱相互作用的固有复杂性，仍然是一项基础但具有挑战性的任务。尽管近期进展探索了多种创新方法，但现有方法在聚类精度、特征判别力、计算效率和对噪声的鲁棒性方面仍存在关键局限，阻碍了其实际部署。本文提出了一种用于高光谱图像的自监督高效低通对比图聚类方法（SLCGC）。我们的方法首先进行同质区域生成，将像素聚合为光谱一致的区域，以保持局部空间-光谱一致性，同时大幅降低图复杂度。随后，我们利用邻接矩阵 A 构建结构图，并引入低通图去噪机制来抑制图拓扑中的高频噪声，确保特征传播的稳定性。我们设计了一个双分支图对比学习模块，其中高斯噪声扰动通过两个多层感知机（MLP）生成增强视图，并通过跨视图对比损失强制视图之间的结构一致性，以学习对噪声不变的表征。最后，由该过程优化的潜在嵌入通过 K-means 进行聚类。大量实验和重复对比分析验证了我们的 SLCGC 具有高聚类精度、低计算复杂度和强鲁棒性。源代码将公开于 https://github.com/DY-HYX。
英文摘要：
Self-supervised hyperspectral image (HSI) clustering remains a fundamental yet challenging task due to the absence of labeled data and the inherent complexity of spatial-spectral interactions. While recent advancements have explored innovative approaches, existing methods face critical limitations in clustering accuracy, feature discriminability, computational efficiency, and robustness to noise, hindering their practical deployment. In this paper, a self-supervised efficient low-pass contrastive graph clustering (SLCGC) is introduced for HSIs. Our approach begins with homogeneous region generation, which aggregates pixels into spectrally consistent regions to preserve local spatial-spectral coherence while drastically reducing graph complexity. We then construct a structural graph using an adjacency matrix A and introduce a low-pass graph denoising mechanism to suppress high-frequency noise in the graph topology, ensuring stable feature propagation. A dual-branch graph contrastive learning module is developed, where Gaussian noise perturbations generate augmented views through two multilayer perceptrons (MLPs), and a cross-view contrastive loss enforces structural consistency between views to learn noise-invariant representations. Finally, latent embeddings optimized by this process are clustered via K-means. Extensive experiments and repeated comparative analysis have verified that our SLCGC contains high clustering accuracy, low computational complexity, and strong robustness. The code source will be available at https://github.com/DY-HYX.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
