In this article, we integrate the spatial-spectral information of hyperspectral image (HSI) samples into nonnegative matrix factorization (NMF) for affinity matrix learning to address the issue of HSI clustering. This technique consists of three main components: 1) oversegmentation for computing the spectral-spatial affinity matrix; 2) NMF with the guidance of the obtained affinity matrix; and 3) density-based spectral clustering on the final affinity matrix. First, the HSI is oversegmented into superpixels via the entropy rate superpixel algorithm. The spectral-spatial affinity matrix is defined based on the class-consistency assumption of all the HSI samples in each superpixel and the similar HSI samples between adjacent superpixels. Second, to integrate the spectral-spatial information into NMF, the obtained affinity matrix is used to guide the iterative process of NMF. The spectral-spatial affinity matrix is then weighted by the affinity matrix in the obtained low-dimensional subspace to form the final affinity matrix. Third, density-based spectral clustering is applied to the final affinity matrix to obtain clustering maps. Experimental results on three public benchmark HSIs demonstrate that the proposed method is superior to the considered state-of-the-art baseline methods on both the computational cost and clustering accuracy.

中文翻译：

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通过用于高光谱图像聚类的非负矩阵分解的亲和矩阵学习

在本文中，我们将高光谱图像 (HSI) 样本的空间光谱信息整合到非负矩阵分解 (NMF) 中以进行亲和矩阵学习，以解决 HSI 聚类问题。该技术由三个主要部分组成：1）用于计算谱空间亲和度矩阵的过分割；2）在得到的亲和度矩阵的指导下进行NMF；3) 最终亲和度矩阵上基于密度的谱聚类。首先，通过熵率超像素算法将 HSI 过分割为超像素。谱空间亲和矩阵是基于每个超像素中所有 HSI 样本和相邻超像素之间相似 HSI 样本的类一致性假设定义的。其次，将光谱空间信息整合到 NMF 中，得到的亲和度矩阵用于指导 NMF 的迭代过程。然后在得到的低维子空间中通过亲和度矩阵对谱空间亲和度矩阵进行加权，形成最终的亲和度矩阵。第三，将基于密度的谱聚类应用于最终的亲和矩阵以获得聚类图。在三个公共基准 HSI 上的实验结果表明，所提出的方法在计算成本和聚类精度方面均优于所考虑的最先进的基线方法。