Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System
Benefiting from rich agricultural land, easy transport and fishing, etc., more and more people are moving to live in coastal areas, with more than 200 million people now living in coastal areas that are vulnerable to extreme sea level events. Sea level information is useful for coastal societies. Im...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/2/391 |
| _version_ | 1797620024318361600 |
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| author | Ying Yang Cunwei Lu Zhenhua Li |
| author_facet | Ying Yang Cunwei Lu Zhenhua Li |
| author_sort | Ying Yang |
| collection | DOAJ |
| description | Benefiting from rich agricultural land, easy transport and fishing, etc., more and more people are moving to live in coastal areas, with more than 200 million people now living in coastal areas that are vulnerable to extreme sea level events. Sea level information is useful for coastal societies. Image measurement is rapidly developing as a new type of measurement tool. A multicamera-based sea level monitoring system along Japan’s coast near the Pacific is proposed in this paper, and a long-distance sea wave matching method for this system is described. The whole system employs multiple binocular vision systems to take sea surface images and obtain the sea level height based on the disparity between the field of views of the left and right cameras, forming a local measurement and overall analysis monitoring system. Sea level monitoring requires a high processing accuracy and speed to realize a timely response to extreme events. Thus, the paper extracts sea waves and integrates a sea wave’s appearance features as feature points and descriptors and pioneers the idea of searching deterministic features for fast image processing. The average stereo matching precision of the proposed method is up to 89.9% with a running time smaller than 40 ms for most pairs of images. Experiments on various real sea surface image pairs are conducted to validate the effectiveness of the proposed method. |
| first_indexed | 2024-03-11T08:35:31Z |
| format | Article |
| id | doaj.art-37af55e2b5ec4c4b8ec20f02d663314b |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T08:35:31Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-37af55e2b5ec4c4b8ec20f02d663314b2023-11-16T21:28:31ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-02-0111239110.3390/jmse11020391Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring SystemYing Yang0Cunwei Lu1Zhenhua Li2Department of Optic Engineering, School of Science, Nanjing University of Science and Technology (NJUST), Nanjing 210092, ChinaInformation and Systems Engineering, Fukuoka Institute of Technology, Fukuoka 811-0295, JapanDepartment of Optic Engineering, School of Science, Nanjing University of Science and Technology (NJUST), Nanjing 210092, ChinaBenefiting from rich agricultural land, easy transport and fishing, etc., more and more people are moving to live in coastal areas, with more than 200 million people now living in coastal areas that are vulnerable to extreme sea level events. Sea level information is useful for coastal societies. Image measurement is rapidly developing as a new type of measurement tool. A multicamera-based sea level monitoring system along Japan’s coast near the Pacific is proposed in this paper, and a long-distance sea wave matching method for this system is described. The whole system employs multiple binocular vision systems to take sea surface images and obtain the sea level height based on the disparity between the field of views of the left and right cameras, forming a local measurement and overall analysis monitoring system. Sea level monitoring requires a high processing accuracy and speed to realize a timely response to extreme events. Thus, the paper extracts sea waves and integrates a sea wave’s appearance features as feature points and descriptors and pioneers the idea of searching deterministic features for fast image processing. The average stereo matching precision of the proposed method is up to 89.9% with a running time smaller than 40 ms for most pairs of images. Experiments on various real sea surface image pairs are conducted to validate the effectiveness of the proposed method.https://www.mdpi.com/2077-1312/11/2/391image measurementsea level monitoringcorrespondence matchingdeterministic features |
| spellingShingle | Ying Yang Cunwei Lu Zhenhua Li Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System Journal of Marine Science and Engineering image measurement sea level monitoring correspondence matching deterministic features |
| title | Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System |
| title_full | Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System |
| title_fullStr | Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System |
| title_full_unstemmed | Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System |
| title_short | Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System |
| title_sort | long distance sea wave sparse matching algorithm for sea level monitoring system |
| topic | image measurement sea level monitoring correspondence matching deterministic features |
| url | https://www.mdpi.com/2077-1312/11/2/391 |
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