Unbalanced Optimal Transport Dictionary Learning for Unsupervised Hyperspectral Image Clustering
This addresses the challenge of labeling-intensive hyperspectral image analysis for remote sensing or environmental monitoring, but it is incremental as it builds on existing dictionary learning methods.
The paper tackled the problem of unsupervised clustering in hyperspectral images by using unbalanced Wasserstein barycenters to learn a lower-dimensional representation, resulting in an effective approach for automated segmentation without requiring labeled data.
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 more 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. However, this approach requires balancing the spectral profiles of the data, blurring the classes, and sacrificing robustness to outliers and noise. In this paper, we suggest improving this approach by utilizing unbalanced Wasserstein barycenters to learn a lower-dimensional representation of the underlying data. The deployment of spectral clustering on the learned representation results in an effective approach for the unsupervised learning of labels.