论文标题
高光谱图像聚类，具有空间规范化的超特质
Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics
论文作者
论文摘要
我们提出了一种基于空间正规化光谱聚类和超级路径距离的空间频谱聚类的无监督聚类的方法。提出的方法有效地结合了数据密度和几何形状，以区分数据中的材料类别，而无需训练标签。所提出的方法是有效的，在数据点的数量中进行了准线性缩放，并享有强大的理论性能保证。与基准和最新方法相比，有关合成和实际HSI数据的广泛实验表明了其强劲的性能。特别是，所提出的方法不仅达到了出色的标记精度，而且还可以有效地估计簇的数量。
We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and geometry to distinguish between material classes in the data, without the need for training labels. The proposed method is efficient, with quasilinear scaling in the number of data points, and enjoys robust theoretical performance guarantees. Extensive experiments on synthetic and real HSI data demonstrate its strong performance compared to benchmark and state-of-the-art methods. In particular, the proposed method achieves not only excellent labeling accuracy, but also efficiently estimates the number of clusters.
