H-means image segmentation to identify solar thermal features

Abstract

Properly segmenting multiband images of the Sun by their thermal properties will help determine the thermal structure of the solar corona. However, off-the-shelf segmentation algorithms are typically inappropriate because temperature information is captured by the relative intensities in different passbands, while the absolute levels are not relevant. Input features are therefore pixel-wise proportions of photons observed in each band. To segment solar images based on these proportions, we use a modification of k-means clustering that we call the H-means algorithm because it uses the Hellinger distance to compare probability vectors. H-means has a closed-form expression for cluster centroids, so computation is as fast as k-means. Tempering the input probability vectors reveals a broader class of H-means algorithms which include spherical k-means clustering. More generally, H-means can be used anytime the input feature is a probabilistic distribution, and hence is useful beyond image segmentation applications.