Ph.D. Dissertation Defense Santosh Suresh A Framework for Near Real Time Feature Detection from the Atmospheric Imaging Assembly Images of theSolar Dynamics Observatory Monday August 12, 2013 11:00 AM 76-2155

CHESTER F CARLSON Center for Imaging Science

Ph.D. Dissertation Defense

Santosh Suresh

A Framework for Near Real Time Feature Detection from the

Atmospheric Imaging Assembly Images of the

Solar Dynamics Observatory

Advisor: Dr. Roger Dube

Monday August 12, 2013 11:00 AM

76-2155

Abstract

The Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) provides high resolution images of the sun imaged at different wavelengths at a rate of approximately one every 10 seconds. Today, the process of identifying features and estimating their properties is applied manually in an iterative fashion to verify the detection results. The study of the variability of the solar corona and the monitoring of its traditional regions (Coronal Holes, Quiet Sun and Active Regions) are of great importance in astrophysics as well as in view of the Space Weather applications

We introduce a complete, automated image-processing pipeline, starting with raw data and ending with quantitative data of high level feature parameters. We propose two multichannel unsupervised algorithms that automatically segments EUV AIA solar images into Coronal Holes, Quiet Sun and Active Regions in near real time. We also propose a method of post processing to deal with fragments in a segmented image by spatial validity based compact clustering.

The segmentation results are consistent with well-known algorithms and databases. The parameters extracted from the segments like area closely follow the solar activity pattern. Moreover, the methods developed within the proposed framework are generic enough to allow the study of any solar feature (e.g. Coronal Bright points) provided that the feature can be segmented from AIA images