Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry
Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry
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DOI：
10.1109/igarss47720.2021.9554397
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Sam L. Polk;James M. Murphy
中科院分区：
文献类型：
--
作者：
Sam L. Polk;James M. Murphy
聚类算法将数据集划分为相似点的组。本文的主要贡献是多尺度空间正则化扩散学习（M-SRDL）聚类算法，该算法利用空间正则化扩散距离高效准确地学习高光谱图像（HSI）中潜在结构的多尺度。M-SRDL聚类算法从HSI中提取多个尺度的聚类，并输出这些聚类的信息重心变化，作为所有底层聚类结构的范例。我们表明，将空间正则化纳入多尺度聚类框架，当应用于HSI数据时，对应于更平滑和更连贯的聚类，并导致更准确的聚类标签。
Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized diffusion distances to efficiently and accurately learn multiple scales of latent structure in hyperspectral images (HSI). The M-SRDL clustering algorithm extracts clusterings at many scales from an HSI and outputs these clusterings' variation of information-barycenter as an exemplar for all underlying cluster structure. We show that incorporating spatial regularization into a multiscale clustering framework corresponds to smoother and more coherent clusters when applied to HSI data and leads to more accurate clustering labels.