Joint Superpixel and Self-Representation Learning for Scalable Hyperspectral Image Clustering

Subspace clustering is effective for hyperspectral image (HSI) analysis, but its high computational 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 Multi-pliers (ADMM) provides a model-driven signal to guide a differentiable superpixel module. This joint optimization yields "clustering-aware" partitions that preserve spectral-spatial structure. Further-more, our superpixel network learns a unique compactness parameter for each superpixel, enabling more flexible and adaptive segmentation. Experiments on benchmark HSI datasets demonstrate that our method consistently achieves superior accuracy compared with state-of-the-art clustering approaches.