联合优化超像素与自表示学习，提升高光谱图像聚类效率与精度
Joint Superpixel and Self-Representation Learning for Scalable Hyperspectral Image Clustering
- 通过反馈机制联合训练超像素分割与子空间聚类
- 在多个基准数据集上达到当前最优聚类准确率
- 适合需要高效高精度聚类的遥感图像分析场景
子空间聚类是高光谱图像（HSI）分析中强大的无监督方法，但其高计算与内存开销限制了可扩展性。超像素分割可通过减少待处理数据点数量来提升效率。然而，现有基于超像素的方法通常独立于聚类任务进行分割，导致划分结果与后续聚类目标不一致。为此，我们提出一个统一的端到端框架，联合优化超像素分割与子空间聚类。核心为反馈机制：基于展开的交替方向乘子法（ADMM）的自表示网络提供模型驱动信号，指导可微分超像素模块。该联合优化生成兼顾聚类目标的分割结果，同时保留光谱与空间结构。此外，超像素网络为每个超像素学习唯一的紧凑性参数，实现更灵活自适应的分割。在多个基准HSI数据集上的大量实验表明，本方法在聚类精度上持续优于当前最先进方法。
原文摘要 · Abstract (English)
Subspace clustering is a powerful unsupervised approach for hyperspectral image (HSI) analysis, but its high computational and memory costs limit scalability. Superpixel segmentation can improve efficiency by reducing the number of data points to process. However, existing superpixel-based methods usually perform segmentation independently of the clustering task, often producing partitions that do not align with the subsequent clustering objective. To address this, we propose a unified end-to-end framework that jointly optimizes superpixel segmentation and subspace clustering. Its core is a feedback mechanism: a self-representation network based on unfolded Alternating Direction Method of Multipliers (ADMM) provides a model-driven signal to guide a differentiable superpixel module. This joint optimization yields clustering-aware partitions that preserve both spectral and spatial structure. Furthermore, our superpixel network learns a unique compactness parameter for each superpixel, enabling more flexible and adaptive segmentation. Extensive experiments on benchmark HSI datasets demonstrate that our method consistently achieves superior accuracy compared with state-of-the-art clustering approaches.
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