Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry
This work addresses clustering challenges in hyperspectral image analysis, offering a domain-specific improvement for remote sensing applications.
The paper tackled the problem of clustering hyperspectral images by developing the M-SRDL algorithm, which uses spatially-regularized diffusion distances to learn multiple scales of latent structure, resulting in smoother and more coherent clusters with improved accuracy.
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.