Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification.
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| Title: | Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification. |
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| Authors: | Jatla, Venkatesh1 (AUTHOR) venkatesh369@unm.edu, Pattichis, Marios S.1 (AUTHOR) pattichi@unm.edu, Arge, Charles Nick2 (AUTHOR) charles.n.arge@nasa.gov |
| Source: | IEEE Transactions on Image Processing. 2020, Vol. 29, p1641-1653. 13p. |
| Subjects: | Random forest algorithms, Linear programming, Image processing, Classification, Image segmentation |
| Abstract: | The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introduce a new method based on Linear Programming for matching clusters of coronal holes. The final matching is then performed using Random Forests. The methods were carefully validated using consensus maps derived from multiple readers, manual clustering, manual map classification, and method validation for 50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN by providing accurate boundary detection. Overall, the method gave a 95.5% map classification accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 170078075 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jatla%2C+Venkatesh%22">Jatla, Venkatesh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> venkatesh369@unm.edu</i><br /><searchLink fieldCode="AR" term="%22Pattichis%2C+Marios+S%2E%22">Pattichis, Marios S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pattichi@unm.edu</i><br /><searchLink fieldCode="AR" term="%22Arge%2C+Charles+Nick%22">Arge, Charles Nick</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> charles.n.arge@nasa.gov</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Image+Processing%22">IEEE Transactions on Image Processing</searchLink>. 2020, Vol. 29, p1641-1653. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+programming%22">Linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introduce a new method based on Linear Programming for matching clusters of coronal holes. The final matching is then performed using Random Forests. The methods were carefully validated using consensus maps derived from multiple readers, manual clustering, manual map classification, and method validation for 50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN by providing accurate boundary detection. Overall, the method gave a 95.5% map classification accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TIP.2019.2944057 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1641 Subjects: – SubjectFull: Random forest algorithms Type: general – SubjectFull: Linear programming Type: general – SubjectFull: Image processing Type: general – SubjectFull: Classification Type: general – SubjectFull: Image segmentation Type: general Titles: – TitleFull: Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jatla, Venkatesh – PersonEntity: Name: NameFull: Pattichis, Marios S. – PersonEntity: Name: NameFull: Arge, Charles Nick IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 10577149 Numbering: – Type: volume Value: 29 Titles: – TitleFull: IEEE Transactions on Image Processing Type: main |
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