Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
This work addresses the need for spatially coherent clustering in applications like hyperspectral imaging, offering an incremental improvement over existing methods.
The paper tackled the problem of clustering datasets with spatial information, such as hyperspectral imaging data, by introducing orthogonal nonnegative matrix factorization models with total variation regularization to enforce spatial coherence. The result was significantly better clustering performance compared to classical methods, as demonstrated on a hyperspectral dataset.
Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not necessarily be reconstructed well by standard clustering methods. Motivated by applications in hyperspectral imaging, we introduce in this work clustering models based on orthogonal nonnegative matrix factorization, which include an additional total variation (TV) regularization procedure on the cluster membership matrix to enforce the needed spatial coherence in the clusters. We propose several approaches with different optimization techniques, where the TV regularization is either performed as a subsequent postprocessing step or included into the clustering algorithm. Finally, we provide a numerical evaluation of all proposed methods on a hyperspectral dataset obtained from a matrix-assisted laser desorption/ionisation imaging measurement, which leads to significantly better clustering results compared to classical clustering models.