Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry

Autor:	Polk, Sam L., Murphy, James M.
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. The M-SRDL clustering algorithm extracts clusterings at many scales from a hyperspectral image 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 results in smoother and more coherent clusters when applied to hyperspectral data, yielding more accurate clustering labels. Comment: (6 pages, 2 figures). Proceedings of IEEE IGARSS 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.15783 Zobrazit plný text záznamu View this record from Arxiv