Clustering is a common method for hyperspectral image (HSI) interpretation in the case of no labeled samples. Many subspace clustering methods have now been proposed for HSIs and have obtained remarkable success. However, because of the prohibitively large computational complexity induced by the self-dictionary representation, these methods suffer from the scalability issue and are ineffective for large HSIs. In this article, to address this issue, we focus on a scalable subspace clustering scheme and introduce the recently developed sketched subspace clustering (sketched-SC) model to HSI. The sketched-SC model is computationally inexpensive and is suitable for the large HSI clustering task as it constructs a compact yet expressive dictionary. However, several problems degrade the performance of sketched-SC, i.e., the inadequate mining of the structural information and no consideration of spatial information. In view of this, a novel scalable nonlocal means regularized sketched reweighted sparse and low-rank (NL-SSLR) SC algorithm is proposed for use with large HSIs. On the one hand, the SSLR representation model is constructed to explore the underlying local and global structural information of the HSIs at the same time. On the other hand, the nonlocal means regularization is used to fully explore the spatial correlation information and better account for the self-similarity of HSIs, to further boost the clustering performance. The experimental results obtained on two well-known hyperspectral data sets corroborate the superiority of the proposed algorithm over the other state-of-the-art HSI clustering methods.

中文翻译：

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大局部高光谱图像的非局部均值正则化草图重加权稀疏和低秩子空间聚类

在没有标记样品的情况下，聚类是解释高光谱图像（HSI）的常用方法。现在已经为HSI提出了许多子空间聚类方法，并获得了显着的成功。但是，由于自字典表示导致的计算量过大，因此这些方法存在可伸缩性问题，并且对于大型HSI无效。在本文中，为了解决此问题，我们将重点放在可伸缩的子空间聚类方案上，并将最新开发的草图子空间聚类（sketched-SC）模型引入HSI。草绘的SC模型在计算上不昂贵，并且适合于大型HSI聚类任务，因为它构建了紧凑而又富有表现力的字典。但是，有几个问题会降低草绘SC的性能，即 对结构信息的挖掘不足，并且不考虑空间信息。鉴于此，提出了一种适用于大型HSI的新颖的可伸缩非局部均值正则化草图重加权稀疏和低秩（NL-SSLR）SC算法。一方面，构建SSLR表示模型以同时探索HSI的基础本地和全局结构信息。另一方面，非局部均值正则化被用于充分探索空间相关信息，并更好地说明HSI的自相似性，从而进一步提高聚类性能。在两个著名的高光谱数据集上获得的实验结果证实了该算法相对于其他最新的HSI聚类方法的优越性。