Structured Anchor Learning for Large-Scale Hyperspectral Image Projected Clustering

Guozhu Jiang, Yongshan Zhang, Xinxin Wang, Xinwei Jiang, Lefei Zhang

Abstract: Hyperspectral image (HSI) clustering has attracted increasing attention in recent years, because it doesn’t rely on labeled pixels. However, it is a challenging task due to the complex spectral-spatial structure. The emergence of large-scale HSIs introduces a new challenge in terms of heightened computational complexity. To address the above challenges, in this paper, we propose a structured anchor projected clustering (SAPC) model for large-scale HSIs. Specifically, we exploit spatial information reflecting in the generated superpixels to perform denoising and generate anchors. Based on the preprocessing, we simultaneously learn a pixel-anchor graph and an anchor-anchor graph in a projected feature space. Meanwhile, the rank-constraint is imposed on the Laplacian matrix related to the anchor-anchor graph. To uncover the clustering structure, we design a clustering inference strategy to propagate clustering labels from anchors to pixels based on the dual graphs. Additionally, we propose an efficient optimization strategy for the formulated SAPC model with linear time complexity in terms of the number of pixels. Since the anchor-anchor graph is with much smaller size, it is high efficient to obtain the structured anchors with pseudo labels. Thus, the clustering process is significantly accelerated. Extensive experiments on multiple large-scale HSI datasets demonstrates the superiority of our SAPC over the state-of-the-art methods. The source code is released at https://github.com/ZhangYongshan/SAPC.