Spectral–Spatial Diffusion Geometry for Hyperspectral Image Clustering
Spectral–Spatial Diffusion Geometry for Hyperspectral Image Clustering
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DOI：
10.1109/lgrs.2019.2943001
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发表时间：
2019-02
影响因子：
4.8
通讯作者：
James M. Murphy;M. Maggioni
中科院分区：
文献类型：
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作者：
James M. Murphy;M. Maggioni
提出了一种利用空间正则化随机游走的无监督学习算法对高光谱图像数据进行聚类。马尔可夫扩散被定义在HSI谱的空间上，其跃迁被约束到附近的空间邻居。将空间规律性明确地结合到扩散结构中，导致更平滑的随机过程，比仅基于光谱的随机过程更适合于无监督机器学习。正则化扩散过程随后用于通过扩散距离将高维HSI嵌入到低维空间中。使用核密度估计和扩散距离计算聚类模式，并根据这些模式标记所有其他点。该方法具有较低的计算复杂度和竞争力对国家的最先进的HSI聚类算法对真实的数据。特别是，所提出的空间正则化赋予非正则化方法的理论和经验优势。
An unsupervised learning algorithm to cluster hyperspectral image (HSI) data that leverages spatially regularized random walks is proposed. Markov diffusions are defined on the space of HSI spectra with transitions constrained to near spatial neighbors. The explicit incorporation of spatial regularity into the diffusion construction leads to smoother random processes that are more adapted for unsupervised machine learning than those based on spectra alone. The regularized diffusion process is subsequently used to embed the high-dimensional HSI into a lower-dimensional space through diffusion distances. Cluster modes are computed using kernel density estimation and diffusion distances, and all other points are labeled according to these modes. The proposed method has low computational complexity and performs competitively against state-of-the-art HSI clustering algorithms on real data. In particular, the proposed spatial regularization confers both theoretical and empirical advantages over nonregularized methods.