Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics
Observatory, a NASA satellite, collect massive streams of high resolution images
of the Sun through multiple wavelength filters. Reconstructing pixel-by-pixel thermal
properties based on these images can be framed as an ill-posed inverse problem with
Poisson noise, but this reconstruction is computationally expensive and there is disagreement
among researchers about what regularization or prior assumptions are most
appropriate. This article presents an image segmentation framework for preprocessing
such images in order to reduce the data volume while preserving as much thermal information
as possible for later downstream analyses. The resulting segmented images
reflect thermal properties but do not depend on solving the ill-posed inverse problem.
This allows users to avoid the Poisson inverse problem altogether or to tackle it on each
of ∼10 segments rather than on each of ∼107 pixels, reducing computing time by a
factor of ∼106
. We employ a parametric class of dissimilarities that can be expressed as
cosine dissimilarity functions or Hellinger distances between nonlinearly transformed
vectors of multi-passband observations in each pixel. We develop a decision theoretic
framework for choosing the dissimilarity that minimizes the expected loss that arises
when estimating identifiable thermal properties based on segmented images rather than
on a pixel-by-pixel basis. We also examine the efficacy of different dissimilarities for
recovering clusters in the underlying thermal properties. The expected losses are computed
under scientifically motivated prior distributions. Two simulation studies guide
our choices of dissimilarity function. We illustrate our method by segmenting images
of a coronal hole observed on 26 February 2015.