Selection of Image Parameters as the First Step towards Creating a CBIR System for the Solar Dynamics Observatory

This work describes the attribute evaluation sections of the ambitious goal of creating a large-scale content-based image retrieval (CBIR) system for solar phenomena in NASA images from the Solar Dynamics Observatory mission. This mission, with its Atmospheric Imaging Assembly (AIA), is generating eight 4096 pixels x 4096 pixels images every 10 seconds, leading to a data transmission rate of approximately 700 Gigabytes per day from only the AIA component (the entire mission is expected to be sending about 1.5 Terabytes of data per day, for a minimum of 5 years). We investigate unsupervised and supervised methods of selecting image parameters and their importance from the perspective of distinguishing between different types of solar phenomena by using correlation analysis, and three supervised attribute evaluation methods. By selecting the most relevant image parameters (out of the twelve tested) we expect to be able to save 540 Megabytes per day of storage costs for each parameter that we remove. In addition, we also applied several image filtering algorithms on these images in order to investigate the enhancement of our classification results. We confirm our experimental results by running multiple classifiers for comparative analysis on the selected image parameters and filters.