Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics
Constant false alarm rate (CFAR) detector is a common method for ship detection in polarimetric synthetic aperture radar (PolSAR) images. CFAR detectors greatly depend on the clutter modeling that can be easily affected by the contamination caused by both lower- and higher-intensity outliers, such a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10410889/ |
| _version_ | 1797316077931200512 |
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| author | Wenxing Mu Ning Wang Lu Fang Tao Liu |
| author_facet | Wenxing Mu Ning Wang Lu Fang Tao Liu |
| author_sort | Wenxing Mu |
| collection | DOAJ |
| description | Constant false alarm rate (CFAR) detector is a common method for ship detection in polarimetric synthetic aperture radar (PolSAR) images. CFAR detectors greatly depend on the clutter modeling that can be easily affected by the contamination caused by both lower- and higher-intensity outliers, such as spilled oil and intensive targets. Traditional CFAR detectors perform detection in a pixel-by-pixel manner, which ignores the spatial information. Both the bias in clutter modeling and the absence of spatial information can degrade the ship target detection performance. In this study, a superpixel-level polarimetric bilateral truncated statistics CFAR detector is proposed to promote the ship target detection performance in complex ocean scenarios. As the preprocessing of the PolSAR image, the superpixel segmentation is conducted based on the multilook polarimetric whitening filter result to select candidate ship target superpixels for bilateral truncation and background clutter modeling. The elliptical truncation is expanded to a complex situation and the relationship between the second moments before and after truncation is derived. The maximum-likelihood estimation estimator of the equivalent number of looks based on the bilateral truncation distribution is derived and compared with other parameter estimators. The influence of the truncation depth on estimator performance is analyzed, according to which the adaptive bilateral truncation method is determined. The Gaussian mixture model and the Parzen window kernel method are compared with the model-based method and utilized for data fitting. The proposed method performs bilateral truncation based on the superpixel segmentation result to provide pure clutter samples for accurate parameter estimation and clutter distribution modeling, reducing time consumption and false alarms. The method is validated efficient on both simulated and measured data from RADARSAT-2. |
| first_indexed | 2024-03-08T03:14:03Z |
| format | Article |
| id | doaj.art-bae9c8f500684b7ca664303c5daa972c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T03:14:03Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-bae9c8f500684b7ca664303c5daa972c2024-02-13T00:00:43ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01174247426210.1109/JSTARS.2024.335659110410889Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated StatisticsWenxing Mu0https://orcid.org/0009-0002-5722-5094Ning Wang1https://orcid.org/0009-0001-0013-4389Lu Fang2https://orcid.org/0000-0001-6268-6173Tao Liu3https://orcid.org/0000-0002-9596-4536School of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaSchool of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaSchool of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaSchool of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaConstant false alarm rate (CFAR) detector is a common method for ship detection in polarimetric synthetic aperture radar (PolSAR) images. CFAR detectors greatly depend on the clutter modeling that can be easily affected by the contamination caused by both lower- and higher-intensity outliers, such as spilled oil and intensive targets. Traditional CFAR detectors perform detection in a pixel-by-pixel manner, which ignores the spatial information. Both the bias in clutter modeling and the absence of spatial information can degrade the ship target detection performance. In this study, a superpixel-level polarimetric bilateral truncated statistics CFAR detector is proposed to promote the ship target detection performance in complex ocean scenarios. As the preprocessing of the PolSAR image, the superpixel segmentation is conducted based on the multilook polarimetric whitening filter result to select candidate ship target superpixels for bilateral truncation and background clutter modeling. The elliptical truncation is expanded to a complex situation and the relationship between the second moments before and after truncation is derived. The maximum-likelihood estimation estimator of the equivalent number of looks based on the bilateral truncation distribution is derived and compared with other parameter estimators. The influence of the truncation depth on estimator performance is analyzed, according to which the adaptive bilateral truncation method is determined. The Gaussian mixture model and the Parzen window kernel method are compared with the model-based method and utilized for data fitting. The proposed method performs bilateral truncation based on the superpixel segmentation result to provide pure clutter samples for accurate parameter estimation and clutter distribution modeling, reducing time consumption and false alarms. The method is validated efficient on both simulated and measured data from RADARSAT-2.https://ieeexplore.ieee.org/document/10410889/Bilateral truncated statistics (BTS)constant false alarm rate (CFAR)superpixelsynthetic aperture radar (SAR) |
| spellingShingle | Wenxing Mu Ning Wang Lu Fang Tao Liu Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Bilateral truncated statistics (BTS) constant false alarm rate (CFAR) superpixel synthetic aperture radar (SAR) |
| title | Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics |
| title_full | Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics |
| title_fullStr | Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics |
| title_full_unstemmed | Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics |
| title_short | Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics |
| title_sort | superpixel level cfar ship detection based on polarimetric bilateral truncated statistics |
| topic | Bilateral truncated statistics (BTS) constant false alarm rate (CFAR) superpixel synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/10410889/ |
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