Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion
Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classificati...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/8/2/99 |
| _version_ | 1818644512757514240 |
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| author | Chenxi Liu Tao Jiang Zhen Zhang Baikai Sui Xinliang Pan Linjing Zhang Jingyu Zhang |
| author_facet | Chenxi Liu Tao Jiang Zhen Zhang Baikai Sui Xinliang Pan Linjing Zhang Jingyu Zhang |
| author_sort | Chenxi Liu |
| collection | DOAJ |
| description | Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance. |
| first_indexed | 2024-12-17T00:16:02Z |
| format | Article |
| id | doaj.art-bbfb6667b663419485d8154a0425747f |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-12-17T00:16:02Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-bbfb6667b663419485d8154a0425747f2022-12-21T22:10:42ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-02-01829910.3390/jmse8020099jmse8020099Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature FusionChenxi Liu0Tao Jiang1Zhen Zhang2Baikai Sui3Xinliang Pan4Linjing Zhang5Jingyu Zhang6College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaMarine Physics and Remote Sensing Research Department, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaMariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance.https://www.mdpi.com/2077-1312/8/2/99offshore mariculturemulti-source featuresweak signal extractionsemantic segmentation |
| spellingShingle | Chenxi Liu Tao Jiang Zhen Zhang Baikai Sui Xinliang Pan Linjing Zhang Jingyu Zhang Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion Journal of Marine Science and Engineering offshore mariculture multi-source features weak signal extraction semantic segmentation |
| title | Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion |
| title_full | Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion |
| title_fullStr | Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion |
| title_full_unstemmed | Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion |
| title_short | Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion |
| title_sort | extraction method of offshore mariculture area under weak signal based on multisource feature fusion |
| topic | offshore mariculture multi-source features weak signal extraction semantic segmentation |
| url | https://www.mdpi.com/2077-1312/8/2/99 |
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