Name
Abstract | ||
|---|---|---|
The automated detection of solar phenomena from high resolution images became important for solar physics researchers after the launch of the Solar Dynamics Observatory. The solar event detections help researchers find and track relevant regions and eventually facilitate the discovery of trends and patterns between different types of events. We address the problem of automated detection of solar events from multi-wavelength solar images using deep learning-based Faster R-CNN method. Earlier work on solar event detection primarily use the observed models to locate the events on the solar images in an unsupervised fashion and each detection algorithm targets specific solar event type. Here, we will present a data-driven methodology to facilitate solar physics research. While this work presents a proof of concept that supervised deep learning-based event detection methodology for solar images is possible, our results show that data-driven detection using deep learning can successfully detect the multiple types of solar events and it can be used for validating the results from existing modules or training modules to detect new event types. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BigData.2017.8258214 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | DocType | ISSN |
Solar Event Detection, Multi-wavelength, SDO AIA data, Deep learning, fast R-CNN. | Conference | 2639-1589 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmet Kucuk | 1 | 3 | 2.42 |
| Berkay Aydin | 2 | 40 | 10.75 |
| Rafal A. Angryk | 3 | 271 | 45.56 |