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Deep learning-based solar image captioning

Title
Deep learning-based solar image captioning
Authors
BaekJi-HyeKimSujinChoiSeonghwanParkJongyeobDongil
Ewha Authors
김동일
SCOPUS Author ID
김동일scopus
Issue Date
2024
Journal Title
Advances in Space Research
ISSN
0273-1177JCR Link
Citation
Advances in Space Research vol. 73, no. 6, pp. 3270 - 3281
Keywords
Deep learningImage captioningSolar dynamics observatorySolar eventSolar image
Publisher
Elsevier Ltd
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Solar images are essential for identifying and predicting solar phenomena, and have been used as key information for analyzing space weather. In this paper, we propose a solar image captioning method that applies a transformer-based deep learning (DL) natural language processing method. In addition, we provide a new DeepSDO description dataset for training solar image captioning models. First, we develop the DeepSDO description dataset using solar image data from Korean Data Center for solar dynamics observatory (SDO) and scripts from the National Aeronautics and Space Administration (NASA) SDO gallery website. The DeepSDO description dataset includes nine solar events: sunspots, flares, prominences, prominent eruptions, coronal holes, coronal loops, filaments, active regions, and eclipses. Second, we train the DL-based image captioning model, the meshed-memory transformer, using the DeepSDO description dataset. The experimental results show that the proposed method outperforms other benchmark methods in terms of four evaluation metrics. This study demonstrates that DL-based image captioning can successfully generate solar image captions for multiple solar features, and could potentially be used in other themes of solar physics and space weather. © 2024 COSPAR
DOI
10.1016/j.asr.2023.12.066
Appears in Collections:
ETC > ETC
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