The unsupervised classification of hyperspectral images (HSIs) draws attention in the remote sensing community due to its inherent complexity and the lack of labeled data. Among unsupervised methods, sparse subspace clustering (SSC) achieves high clustering accuracy by constructing a sparse affinity matrix. However, SSC has limitations when clustering HSI images due to the number of spectral pixels. Specifically, the temporal complexity grows at a cubic ratio of the size of the data, making it inefficient for addressing HSI subspace clustering. We propose an efficient SSC-based method that significantly reduces the temporal and spatial computational complexity by splitting the HSI clustering task using similarity-constrained sampling. Our similarity-constrained sampling strategy considers both edge and superpixel information of the HSI to boost the clustering performance. This sampling strategy enables an intelligent selection of spectral signatures, and then, we split the clustering problem into multiples threads. Experimental results on widely used HSI datasets show that the efficiency of the proposed method outperforms baseline methods by up to 30% in overall accuracy and up to six times in computing time.

中文翻译：

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使用相似性约束采样对高光谱图像进行高效子空间聚类

高光谱图像 (HSI) 的无监督分类由于其固有的复杂性和缺乏标记数据而引起了遥感界的关注。在无监督方法中，稀疏子空间聚类（SSC）通过构建稀疏亲和矩阵来实现高聚类精度。然而，由于光谱像素的数量，SSC 在对 HSI 图像进行聚类时存在局限性。具体来说，时间复杂性以数据大小的立方比增长，使其无法解决 HSI 子空间聚类问题。我们提出了一种基于 SSC 的高效方法，该方法通过使用相似性约束采样拆分 HSI 聚类任务来显着降低时间和空间计算复杂度。我们的相似性约束采样策略同时考虑了 HSI 的边缘和超像素信息，以提高聚类性能。这种采样策略可以智能选择光谱特征，然后，我们将聚类问题拆分为多个线程。在广泛使用的 HSI 数据集上的实验结果表明，所提出方法的效率在整体精度上比基线方法高 30%，在计算时间上高出 6 倍。