Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network
The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/134 |
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| author | Pan Xu Dongbao Gao Shui Yu Guangming Li Yun Zhao Guojun Xu |
| author_facet | Pan Xu Dongbao Gao Shui Yu Guangming Li Yun Zhao Guojun Xu |
| author_sort | Pan Xu |
| collection | DOAJ |
| description | The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. Specifically, the U-shaped end-to-end neural network which contains down-sampling and up-sampling parts is proposed to improve the physical spatial resolution limited by the array aperture. The single target and multiple cases are considered separately. In both cases, the normalized loss on the testing sets declines rapidly, and the predicted high-resolution images own great agreement with the ground truth eventually. Further improvements in resolution are focused on, that is, compressing the predicted high-resolution image to its physical spatial resolution limitation. The results show that the trained end-to-end neural network could map high resolution targets to the impulse responses at the same location and amplitude with an uncertain target number. The proposed convolutional neural network approach could give a practical alternative to improve the physical spatial resolution of underwater sonar images. |
| format | Article |
| id | doaj-art-9e07f677750044d391ce4b0e137e4131 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-9e07f677750044d391ce4b0e137e41312025-01-24T13:36:59ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113113410.3390/jmse13010134Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural NetworkPan Xu0Dongbao Gao1Shui Yu2Guangming Li3Yun Zhao4Guojun Xu5College of Meteorology and Ocean, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Ocean, National University of Defense Technology, Changsha 410073, ChinaSchool of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaNational Innovation Institute of Defense Technology, Beijing 100071, ChinaCollege of Meteorology and Ocean, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Ocean, National University of Defense Technology, Changsha 410073, ChinaThe sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. Specifically, the U-shaped end-to-end neural network which contains down-sampling and up-sampling parts is proposed to improve the physical spatial resolution limited by the array aperture. The single target and multiple cases are considered separately. In both cases, the normalized loss on the testing sets declines rapidly, and the predicted high-resolution images own great agreement with the ground truth eventually. Further improvements in resolution are focused on, that is, compressing the predicted high-resolution image to its physical spatial resolution limitation. The results show that the trained end-to-end neural network could map high resolution targets to the impulse responses at the same location and amplitude with an uncertain target number. The proposed convolutional neural network approach could give a practical alternative to improve the physical spatial resolution of underwater sonar images.https://www.mdpi.com/2077-1312/13/1/134underwater sonar imagingphysical spatial resolutionsynthetic aperture sonarconvolutional neural network |
| spellingShingle | Pan Xu Dongbao Gao Shui Yu Guangming Li Yun Zhao Guojun Xu Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network Journal of Marine Science and Engineering underwater sonar imaging physical spatial resolution synthetic aperture sonar convolutional neural network |
| title | Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network |
| title_full | Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network |
| title_fullStr | Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network |
| title_full_unstemmed | Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network |
| title_short | Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network |
| title_sort | enhancing physical spatial resolution of synthetic aperture sonar images based on convolutional neural network |
| topic | underwater sonar imaging physical spatial resolution synthetic aperture sonar convolutional neural network |
| url | https://www.mdpi.com/2077-1312/13/1/134 |
| work_keys_str_mv | AT panxu enhancingphysicalspatialresolutionofsyntheticaperturesonarimagesbasedonconvolutionalneuralnetwork AT dongbaogao enhancingphysicalspatialresolutionofsyntheticaperturesonarimagesbasedonconvolutionalneuralnetwork AT shuiyu enhancingphysicalspatialresolutionofsyntheticaperturesonarimagesbasedonconvolutionalneuralnetwork AT guangmingli enhancingphysicalspatialresolutionofsyntheticaperturesonarimagesbasedonconvolutionalneuralnetwork AT yunzhao enhancingphysicalspatialresolutionofsyntheticaperturesonarimagesbasedonconvolutionalneuralnetwork AT guojunxu enhancingphysicalspatialresolutionofsyntheticaperturesonarimagesbasedonconvolutionalneuralnetwork |