Hierarchical Sampling Representation Detector for Ship Detection in SAR Images
Ship detection achieves great significance in remote sensing of synthetic aperture radar (SAR) and many efforts have been done in recent years. However, distinguishing ship targets precisely from the interference of multiplicative non-Gaussian coherent speckle is still a challenging task due to the...
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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 |
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| Online Access: | https://ieeexplore.ieee.org/document/10733998/ |
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| author | Ming Tong Shenghua Fan Jiu Jiang Chu He |
| author_facet | Ming Tong Shenghua Fan Jiu Jiang Chu He |
| author_sort | Ming Tong |
| collection | DOAJ |
| description | Ship detection achieves great significance in remote sensing of synthetic aperture radar (SAR) and many efforts have been done in recent years. However, distinguishing ship targets precisely from the interference of multiplicative non-Gaussian coherent speckle is still a challenging task due to the discreteness, variability, and nonlinearity of ship scattering features. A detection framework based on hierarchical sampling representation is introduced to alleviate the phenomenon in this article. First, ships in SAR images exhibit multiplicative non-Gaussian coherent speckle, which introduces nonlinear characteristics under the imaging mechanism of SAR. Therefore, a statistical feature learning module is proposed with a learnable design to describe the nonlinear representations and expand the feature space. Second, our method designs a convex-hull representation to fit the irregular contours of ships represented by strong scattering points. Third, in order to supervise and optimize the regression of convex-hull representation, a sparse low-rank reassignment module is employed to evaluate the positive samples with SAR mechanism and reassign ones of high quality, which produces better results. Furthermore, experimental results on three authoritative SAR-oriented datasets for ship detection application present the comprehensive performance of our method. |
| format | Article |
| id | doaj-art-4ea564427f4c453ca545e26e9cdd596d |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| 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-4ea564427f4c453ca545e26e9cdd596d2025-08-20T02:13:52ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117195301954710.1109/JSTARS.2024.348573410733998Hierarchical Sampling Representation Detector for Ship Detection in SAR ImagesMing Tong0https://orcid.org/0009-0002-7302-652XShenghua Fan1Jiu Jiang2https://orcid.org/0009-0001-6776-2152Chu He3https://orcid.org/0000-0003-3662-5769School of Electronic Information, Wuhan University, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaSchool of Electronic Information, Wuhan University, Wuhan, ChinaSchool of Electronic Information, Wuhan University, Wuhan, ChinaShip detection achieves great significance in remote sensing of synthetic aperture radar (SAR) and many efforts have been done in recent years. However, distinguishing ship targets precisely from the interference of multiplicative non-Gaussian coherent speckle is still a challenging task due to the discreteness, variability, and nonlinearity of ship scattering features. A detection framework based on hierarchical sampling representation is introduced to alleviate the phenomenon in this article. First, ships in SAR images exhibit multiplicative non-Gaussian coherent speckle, which introduces nonlinear characteristics under the imaging mechanism of SAR. Therefore, a statistical feature learning module is proposed with a learnable design to describe the nonlinear representations and expand the feature space. Second, our method designs a convex-hull representation to fit the irregular contours of ships represented by strong scattering points. Third, in order to supervise and optimize the regression of convex-hull representation, a sparse low-rank reassignment module is employed to evaluate the positive samples with SAR mechanism and reassign ones of high quality, which produces better results. Furthermore, experimental results on three authoritative SAR-oriented datasets for ship detection application present the comprehensive performance of our method.https://ieeexplore.ieee.org/document/10733998/Convex-hullship detectionsparse and low-rankstatistical feature learningsynthetic aperture radar (SAR) |
| spellingShingle | Ming Tong Shenghua Fan Jiu Jiang Chu He Hierarchical Sampling Representation Detector for Ship Detection in SAR Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convex-hull ship detection sparse and low-rank statistical feature learning synthetic aperture radar (SAR) |
| title | Hierarchical Sampling Representation Detector for Ship Detection in SAR Images |
| title_full | Hierarchical Sampling Representation Detector for Ship Detection in SAR Images |
| title_fullStr | Hierarchical Sampling Representation Detector for Ship Detection in SAR Images |
| title_full_unstemmed | Hierarchical Sampling Representation Detector for Ship Detection in SAR Images |
| title_short | Hierarchical Sampling Representation Detector for Ship Detection in SAR Images |
| title_sort | hierarchical sampling representation detector for ship detection in sar images |
| topic | Convex-hull ship detection sparse and low-rank statistical feature learning synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/10733998/ |
| work_keys_str_mv | AT mingtong hierarchicalsamplingrepresentationdetectorforshipdetectioninsarimages AT shenghuafan hierarchicalsamplingrepresentationdetectorforshipdetectioninsarimages AT jiujiang hierarchicalsamplingrepresentationdetectorforshipdetectioninsarimages AT chuhe hierarchicalsamplingrepresentationdetectorforshipdetectioninsarimages |