Hyperspectral Image Clustering Via Learned Representation In Wasserstein Space

Hyperspectral images (HSI) capture rich information of large spatial scenes, yet generating labeled training data can be expensive and time-consuming. Unsupervised clustering of HSI allows for segmentation in the absence of labels and is an important problem in processing rapidly collected HSI. In order to accurately cluster noisy and high-dimensional HSI, meaningful data representations that capture latent intrinsic structure must be developed. We propose to leverage regularized dictionary learning in Wasserstein space to efficiently and accurately cluster HSI by modeling HSI pixels as probability distributions. We characterize pixels as similar if they can be synthesized as entropic Wasserstein barycenters with a common set of learned reference distributions. Our approach learns representations that preserve the geometry of the space of HSI spectra and our barycentric coding spectral clustering algorithm, which leverages these learned features, shows promise on benchmark HSI data.