Preprocessing solar images while preserving their latent structure

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Title: Preprocessing solar images while preserving their latent structure
Authors: Stein, NM
Van Dyk, DA
Kashyap, VL
Item Type: Journal Article
Abstract: 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.
Issue Date: 1-Jan-2016
Date of Acceptance: 10-Dec-2015
URI: http://hdl.handle.net/10044/1/29536
DOI: https://dx.doi.org/10.4310/SII.2016.v9.n4.a12
ISSN: 1938-7997
Publisher: International Press
Start Page: 535
End Page: 551
Journal / Book Title: Statistics and its Interface
Volume: 9
Issue: 4
Copyright Statement: © 2016 International Press of Boston, Inc. All rights reserved.
Sponsor/Funder: The Royal Society
Commission of the European Communities
National Science Foundation (US)
Funder's Grant Number: WM110023
FP7-PEOPLE-2012-CIG-321865
DMS 15-13484
Keywords: Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Mathematical & Computational Biology
Mathematics, Interdisciplinary Applications
Mathematics
Clustering
Decision theory
Dissimilarity measure
Hellinger distance
Image segmentation
Latent structure
Solar physics
Space weather
EXTREME-ULTRAVIOLET SPECTRA
DENSITY STRUCTURE
QUIET-SUN
DATA SET
PLASMA
SEGMENTATION
TEMPERATURE
DIAGNOSTICS
CALIBRATION
STATISTICS
Publication Status: Published
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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