A Comparative Evaluation of Automated Solar Filament Detection
Abstract
We present a comparative evaluation for automated filament detection in H-alpha solar images. By using metadata produced by the Advanced Automated Filament Detection and Characterization Code (AAFDCC) module, we adapted our Trainable Feature Recognition (TFR) component to accurately detect regions in solar images containing filaments. We first analyze the module's metadata and then transform it into labeled datasets for machine learning classification. Visualizations of data transformations and classification results are presented and accompanied by statistical findings. Our results confirm the reliable event reporting of the AAFDCC module as well as our ability to effectively detect solar filaments with our TFR component.
- Publication:
-
American Astronomical Society Meeting Abstracts #220
- Pub Date:
- May 2012
- Bibcode:
- 2012AAS...22020105S