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Telescopes such as the Atmospheric Imaging Assembly aboard the Solar DynamicsObservatory, a NASA satellite, collect massive streams of high resolutionimages of the Sun through multiple wavelength filters. Reconstructingpixel-by-pixel thermal properties based on these images can be framed as anill-posed inverse problem with Poisson noise, but this reconstruction iscomputationally expensive and there is disagreement among researchers aboutwhat regularization or prior assumptions are most appropriate. This articlepresents an image segmentation framework for preprocessing such images in orderto reduce the data volume while preserving as much thermal information aspossible for later downstream analyses. The resulting segmented images reflectthermal properties but do not depend on solving the ill-posed inverse problem.This allows users to avoid the Poisson inverse problem altogether or to tackleit on each of $\sim$10 segments rather than on each of $\sim$10$^7$ pixels,reducing computing time by a factor of $\sim$10$^6$. We employ a parametricclass of dissimilarities that can be expressed as cosine dissimilarityfunctions or Hellinger distances between nonlinearly transformed vectors ofmulti-passband observations in each pixel. We develop a decision theoreticframework for choosing the dissimilarity that minimizes the expected loss thatarises when estimating identifiable thermal properties based on segmentedimages rather than on a pixel-by-pixel basis. We also examine the efficacy ofdifferent dissimilarities for recovering clusters in the underlying thermalproperties. The expected losses are computed under scientifically motivatedprior distributions. Two simulation studies guide our choices of dissimilarityfunction. We illustrate our method by segmenting images of a coronal holeobserved on 26 February 2015.

Preprocessing Solar Images while Preserving their Latent Structure