Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Preprocessing Solar Images while Preserving their Latent Structure   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 $\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 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.

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The categories of this paper are: cs
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: eess