SPACE-SUIT: An Artificial Intelligence Based Chromospheric Feature Extractor and Classifier for SUIT Article Swipe
Pranava Seth
,
Vishal Upendran
,
M. Vijay Anand
,
Janmejoy Sarkar
,
Soumya Roy
,
Priyadarshan Chaki
,
P. Chowdhury
,
Basudev Ghosh
,
Durgesh Tripathi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.08589
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.08589
The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structures requires a large sample statistical study, necessitating the development of automatic feature detection methods. To this end, we develop the feature detection algorithm SPACE-SUIT: Solar Phenomena Analysis and Classification using Enhanced vision techniques for SUIT, to detect and classify the solar chromospheric features to be observed from SUIT's Mg II k filter. Specifically, we target plage regions, sunspots, filaments, and off-limb structures. SPACE uses YOLO, a neural network-based model to identify regions of interest. We train and validate SPACE using mock-SUIT images developed from Interface Region Imaging Spectrometer(IRIS) full-disk mosaic images in Mg II k line, while we also perform detection on Level-1 SUIT data. SPACE achieves an approximate precision of 0.788, recall 0.863 and MAP of 0.874 on the validation mock SUIT FITS dataset. Given the manual labeling of our dataset, we perform "self-validation" by applying statistical measures and Tamura features on the ground truth and predicted bounding boxes. We find the distributions of entropy, contrast, dissimilarity, and energy to show differences in the features. These differences are qualitatively captured by the detected regions predicted by SPACE and validated with the observed SUIT images, even in the absence of labeled ground truth. This work not only develops a chromospheric feature extractor but also demonstrates the effectiveness of statistical metrics and Tamura features for distinguishing chromospheric features, offering independent validation for future detection schemes.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.08589
- https://arxiv.org/pdf/2412.08589
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405310909
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4405310909Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.08589Digital Object Identifier
- Title
-
SPACE-SUIT: An Artificial Intelligence Based Chromospheric Feature Extractor and Classifier for SUITWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-11Full publication date if available
- Authors
-
Pranava Seth, Vishal Upendran, M. Vijay Anand, Janmejoy Sarkar, Soumya Roy, Priyadarshan Chaki, P. Chowdhury, Basudev Ghosh, Durgesh TripathiList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.08589Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.08589Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2412.08589Direct OA link when available
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Extractor, Artificial intelligence, Classifier (UML), Computer science, Space (punctuation), Pattern recognition (psychology), Engineering, Operating system, Process engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.features, | 257 |
| abstract_inverted_index.features. | 206 |
| abstract_inverted_index.full-disk | 129 |
| abstract_inverted_index.interest. | 114 |
| abstract_inverted_index.mock-SUIT | 121 |
| abstract_inverted_index.precision | 150 |
| abstract_inverted_index.predicted | 188, 216 |
| abstract_inverted_index.sunspots, | 98 |
| abstract_inverted_index.validated | 220 |
| abstract_inverted_index.filaments, | 99 |
| abstract_inverted_index.properties | 34 |
| abstract_inverted_index.structures | 40 |
| abstract_inverted_index.techniques | 73 |
| abstract_inverted_index.validation | 161, 260 |
| abstract_inverted_index.wavelength | 21 |
| abstract_inverted_index.SPACE-SUIT: | 64 |
| abstract_inverted_index.Ultraviolet | 2 |
| abstract_inverted_index.approximate | 149 |
| abstract_inverted_index.development | 49 |
| abstract_inverted_index.differences | 203, 208 |
| abstract_inverted_index.independent | 259 |
| abstract_inverted_index.photosphere | 14 |
| abstract_inverted_index.statistical | 45, 178, 249 |
| abstract_inverted_index.structures. | 102 |
| abstract_inverted_index.chromosphere | 16 |
| abstract_inverted_index.demonstrates | 245 |
| abstract_inverted_index.observations | 18 |
| abstract_inverted_index.photospheric | 38 |
| abstract_inverted_index.Specifically, | 93 |
| abstract_inverted_index.chromospheric | 36, 82, 240, 256 |
| abstract_inverted_index.comprehensive | 27 |
| abstract_inverted_index.distributions | 194 |
| abstract_inverted_index.effectiveness | 247 |
| abstract_inverted_index.morphological | 39 |
| abstract_inverted_index.necessitating | 47 |
| abstract_inverted_index.network-based | 108 |
| abstract_inverted_index.qualitatively | 210 |
| abstract_inverted_index.thermodynamic | 33 |
| abstract_inverted_index.understanding | 28 |
| abstract_inverted_index.Classification | 69 |
| abstract_inverted_index.dissimilarity, | 198 |
| abstract_inverted_index.distinguishing | 255 |
| abstract_inverted_index.Telescope(SUIT) | 4 |
| abstract_inverted_index."self-validation" | 175 |
| abstract_inverted_index.Spectrometer(IRIS) | 128 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |