SQNet: Simple and Fast Model for Ocean Front Identification
The ocean front has a non-negligible role in global ocean–atmosphere interactions, marine fishery production, and the marine military. Hence, obtaining the positions of the ocean front is crucial in oceanic research. At present, the positioning method of recognizing an ocean front has achieved a bre...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2339 |
| _version_ | 1797601771776901120 |
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| author | Rui Niu Yiyang Tan Feng Ye Fang Gong Haiqing Huang Qiankun Zhu Zengzhou Hao |
| author_facet | Rui Niu Yiyang Tan Feng Ye Fang Gong Haiqing Huang Qiankun Zhu Zengzhou Hao |
| author_sort | Rui Niu |
| collection | DOAJ |
| description | The ocean front has a non-negligible role in global ocean–atmosphere interactions, marine fishery production, and the marine military. Hence, obtaining the positions of the ocean front is crucial in oceanic research. At present, the positioning method of recognizing an ocean front has achieved a breakthrough in the mean dice similarity coefficient (<i>mDSC</i>) of above 90%, but it is difficult to use to achieve rapid extraction in emergency scenarios, including marine fisheries and search and rescue. To reduce the its dependence on machines and apply it to more requirements, according to the characteristics of an ocean front, a multi-scale model SQNet (Simple and Quick Net) dedicated to ocean front position recognition is designed, and its perception domain is expanded while obtaining current scale data. In experiments along the coast of China and the waters of the Gulf of Mexico, it was not difficult to find that SQNet exceedingly reduced running time while ensuring high-precision results (mDSC of higher than 90%). Then, after conducting intra-model self-comparison, it was determined that expanding the perceptual domain and changing the weight ratio of the loss function could improve the accuracy and operational efficiency of the model, which could be better applied in ocean front recognition. |
| first_indexed | 2024-03-11T04:08:31Z |
| format | Article |
| id | doaj.art-dfa360d8d72d46b18a715b9a62871e6b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:31Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dfa360d8d72d46b18a715b9a62871e6b2023-11-17T23:38:46ZengMDPI AGRemote Sensing2072-42922023-04-01159233910.3390/rs15092339SQNet: Simple and Fast Model for Ocean Front IdentificationRui Niu0Yiyang Tan1Feng Ye2Fang Gong3Haiqing Huang4Qiankun Zhu5Zengzhou Hao6State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaThe ocean front has a non-negligible role in global ocean–atmosphere interactions, marine fishery production, and the marine military. Hence, obtaining the positions of the ocean front is crucial in oceanic research. At present, the positioning method of recognizing an ocean front has achieved a breakthrough in the mean dice similarity coefficient (<i>mDSC</i>) of above 90%, but it is difficult to use to achieve rapid extraction in emergency scenarios, including marine fisheries and search and rescue. To reduce the its dependence on machines and apply it to more requirements, according to the characteristics of an ocean front, a multi-scale model SQNet (Simple and Quick Net) dedicated to ocean front position recognition is designed, and its perception domain is expanded while obtaining current scale data. In experiments along the coast of China and the waters of the Gulf of Mexico, it was not difficult to find that SQNet exceedingly reduced running time while ensuring high-precision results (mDSC of higher than 90%). Then, after conducting intra-model self-comparison, it was determined that expanding the perceptual domain and changing the weight ratio of the loss function could improve the accuracy and operational efficiency of the model, which could be better applied in ocean front recognition.https://www.mdpi.com/2072-4292/15/9/2339deeply learningocean frontsimplehigh efficiency |
| spellingShingle | Rui Niu Yiyang Tan Feng Ye Fang Gong Haiqing Huang Qiankun Zhu Zengzhou Hao SQNet: Simple and Fast Model for Ocean Front Identification Remote Sensing deeply learning ocean front simple high efficiency |
| title | SQNet: Simple and Fast Model for Ocean Front Identification |
| title_full | SQNet: Simple and Fast Model for Ocean Front Identification |
| title_fullStr | SQNet: Simple and Fast Model for Ocean Front Identification |
| title_full_unstemmed | SQNet: Simple and Fast Model for Ocean Front Identification |
| title_short | SQNet: Simple and Fast Model for Ocean Front Identification |
| title_sort | sqnet simple and fast model for ocean front identification |
| topic | deeply learning ocean front simple high efficiency |
| url | https://www.mdpi.com/2072-4292/15/9/2339 |
| work_keys_str_mv | AT ruiniu sqnetsimpleandfastmodelforoceanfrontidentification AT yiyangtan sqnetsimpleandfastmodelforoceanfrontidentification AT fengye sqnetsimpleandfastmodelforoceanfrontidentification AT fanggong sqnetsimpleandfastmodelforoceanfrontidentification AT haiqinghuang sqnetsimpleandfastmodelforoceanfrontidentification AT qiankunzhu sqnetsimpleandfastmodelforoceanfrontidentification AT zengzhouhao sqnetsimpleandfastmodelforoceanfrontidentification |