Unbalanced Optimal Transport Dictionary Learning for Unsupervised Hyperspectral Image Clustering

ArXi:2603.10132v1 Announce Type: cross Hyperspectral images capture vast amounts of high-dimensional spectral information about a scene, making labeling an intensive task that is resistant to out-of-the-box statistical methods. Unsupervised learning of clusters allows for automated segmentation of the scene, enabling a rapid understanding of the image. Partitioning the spectral information contained within the data via dictionary learning in Wasserstein space has proven an effective method for unsupervised clustering.