Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method
Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/1/26 |
| _version_ | 1797497814632103936 |
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| author | Weixin Li Ming Li Lei Zuo Hao Sun Hongmeng Chen Yachao Li |
| author_facet | Weixin Li Ming Li Lei Zuo Hao Sun Hongmeng Chen Yachao Li |
| author_sort | Weixin Li |
| collection | DOAJ |
| description | Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of the target has an important effect, for example, in the autonomous landing on a ship. This paper aims to realize the forward-looking imaging of a sea-surface target. In this paper, a multi-prior Bayesian method, which considers the environment and fuses the contour information and the sparsity of the sea-surface target, is proposed. Firstly, due to the imaging environment in which more than one kind of clutter exists, we introduce the Gaussian mixture model (GMM) as the prior information to describe the interference of the clutter and noise. Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. Finally, to solve the optimal parameters, the maximum posterior-expectation maximization (MAP-EM) method is utilized. Experimental results illustrate that the multi-prior Bayesian method can enhance the azimuth resolution, and preserve the contour information of the sea-surface target. |
| first_indexed | 2024-03-10T03:24:35Z |
| format | Article |
| id | doaj.art-f2caab99c02f46ad925b03f7b9ed378a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:24:35Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f2caab99c02f46ad925b03f7b9ed378a2023-11-23T12:11:58ZengMDPI AGRemote Sensing2072-42922021-12-011412610.3390/rs14010026Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian MethodWeixin Li0Ming Li1Lei Zuo2Hao Sun3Hongmeng Chen4Yachao Li5National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaBeijing Institute of Radio Measurement, Beijing 100854, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaTraditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of the target has an important effect, for example, in the autonomous landing on a ship. This paper aims to realize the forward-looking imaging of a sea-surface target. In this paper, a multi-prior Bayesian method, which considers the environment and fuses the contour information and the sparsity of the sea-surface target, is proposed. Firstly, due to the imaging environment in which more than one kind of clutter exists, we introduce the Gaussian mixture model (GMM) as the prior information to describe the interference of the clutter and noise. Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. Finally, to solve the optimal parameters, the maximum posterior-expectation maximization (MAP-EM) method is utilized. Experimental results illustrate that the multi-prior Bayesian method can enhance the azimuth resolution, and preserve the contour information of the sea-surface target.https://www.mdpi.com/2072-4292/14/1/26sea-surface targetGaussian mixture modelsparsetotal variationforward-looking imaging |
| spellingShingle | Weixin Li Ming Li Lei Zuo Hao Sun Hongmeng Chen Yachao Li Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method Remote Sensing sea-surface target Gaussian mixture model sparse total variation forward-looking imaging |
| title | Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method |
| title_full | Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method |
| title_fullStr | Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method |
| title_full_unstemmed | Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method |
| title_short | Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method |
| title_sort | forward looking super resolution imaging for sea surface target with multi prior bayesian method |
| topic | sea-surface target Gaussian mixture model sparse total variation forward-looking imaging |
| url | https://www.mdpi.com/2072-4292/14/1/26 |
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