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- W4287252022 abstract "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." @default.
- W4287252022 created "2022-07-25" @default.
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- W4287252022 date "2021-03-29" @default.
- W4287252022 modified "2023-09-29" @default.
- W4287252022 title "Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry" @default.
- W4287252022 doi "https://doi.org/10.48550/arxiv.2103.15783" @default.
- W4287252022 hasPublicationYear "2021" @default.
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