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Polarimetric SAR Ship Detection Based on Polarimetric Rotation Domain Features and Superpixel Technique
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摘要: 对海监视是极化SAR的重要应用，密集区域的舰船目标检测是当前面临的主要挑战之一。舰船密集区域受多目标串扰，传统的恒虚警率(CFAR)检测滑窗难以选取纯净的海杂波样本用于确定检测门限，将导致检测性能下降。针对这一问题，该文从特征提取和检测器设计两方面出发，提出一种融合极化旋转域特征和超像素技术的极化SAR舰船检测方法。在特征提取方面，雷达目标的后向散射敏感于目标姿态与雷达视线的相对几何关系，由此带来的散射多样性隐含信息可通过极化旋转域分析进行挖掘。该文利用极化相关方向图及导出的一系列极化旋转域特征，根据目标杂波比(TCR)分析，优选TCR最高的3个极化特征量用于构建目标检测器。在此基础上，该文在检测器设计方面提出了一种基于K均值聚类的杂波超像素筛选方法，有效避免了密集区域舰船目标对邻近杂波的影响，基于筛选的杂波像素点得到舰船目标CFAR检测结果。基于Radarsat-2和高分三号星载全极化SAR数据的对比实验表明，所提方法能有效实现密集区域舰船目标检测，检测品质因数达到95%。Abstract: Sea surveillance is an important application of polarimetric Synthetic Aperture Radar (SAR), but ship detection in dense areas remains a major challenge. Due to the crosstalk of multiple targets in dense ship areas, it can be difficult to collect pure sea clutter samples for threshold determination when using the traditional Constant False Alarm Rate (CFAR) moving window, which decreases the detection performance. To address this issue, in this paper, a polarimetric SAR ship detection method is proposed based on polarimetric rotation domain features and superpixel technique, with consideration of both feature selection and detector design. For feature selection, the backscattering of radar targets is sensitive to the relative geometry between the target orientations and the radar line of sight. The information hidden in this scattering diversity can be mined using polarimetric rotation domain analysis, from which the polarimetric correlation pattern and a set of polarimetric rotation domain features are obtained. Target-to-Clutter Ratio (TCR) analysis is conducted, and the three polarimetric features with the highest TCR values are selected for successive target detection. On this basis, a clutter superpixel selection method is developed for detector design based on K-means clustering, which effectively circumvents the influence of dense ship targets on near sea clutter. CFAR ship detection results can be obtained based on the selected clutter samples. Experimental studies on spaceborne Radarsat-2 and GaoFen-3 full polarimetric SAR datasets indicate that, the proposed method can effectively detect dense ship targets with 95% higher figures of merit.
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图 3 Radarsat-2数据及其超像素分割结果
Figure 3. Radarsat-2 data and its superpixel segmentation results
图 4 融合极化旋转域特征和超像素技术的舰船检测方法流程图
Figure 4. Flowchart of ship detection method combing polarimetric rotation domain features and superpixel technique
图 8 Radarsat-2数据对比方法检测结果
Figure 8. Detection results of comparative methods with Radarsat-2 data
图 9 高分三号数据I对比方法检测结果
Figure 9. Detection results of comparative methods with GaoFen-3 data I
图 10 高分三号数据II对比方法检测结果
Figure 10. Detection results of comparative methods with GaoFen-3 data II
表 1 Radarsat-2数据定量检测结果
Table 1. Quantitative detection results of Radarsat-2 data
方法 ${N_{\rm{C}}}$ ${N_{\rm{M}}}$ ${N_{{\rm{FA}}}}$ FoM (%) SO-CFAR方法[38] 126 11 0 91.97 迭代CA-CFAR方法[40] 114 23 0 83.21 显著性方法[12] 116 21 0 84.67 SPAN+超像素 133 4 0 97.08 ${\left| { {\gamma _{ {\rm{HH\text{-}HV} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素 136 1 0 99.27 ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{min} } } }$+超像素 135 2 3 96.43 ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-} (HV)} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素 136 1 1 98.55 多特征融合+超像素 135 2 1 97.83 
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表 2 高分三号数据I定量检测结果
Table 2. Quantitative detection results of GaoFen-3 data I
方法 ${N_{\rm{C}}}$ ${N_{\rm{M}}}$ ${N_{{\rm{FA}}}}$ FoM (%) SO-CFAR方法[38] 183 59 0 75.62 迭代CA-CFAR方法[40] 166 76 0 68.60 显著性方法[12] 215 27 0 88.84 SPAN+超像素 174 68 0 71.90 ${\left| { {\gamma _{ {\rm{HH\text{-}HV} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素 240 2 10 95.24 ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{min} } } }$+超像素 238 4 8 95.20 ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素 241 1 11 95.26 多特征融合+超像素 239 3 8 95.60
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表 3 高分三号数据II定量检测结果
Table 3. Quantitative detection results of GaoFen-3 data II
方法 ${N_{\rm{C}}}$ ${N_{\rm{M}}}$ ${N_{{\rm{FA}}}}$ FoM (%) SO-CFAR方法[38] 38 6 1 84.44 迭代CA-CFAR方法[40] 41 3 6 82.00 显著性方法[12] 31 13 0 70.45 SPAN+超像素 38 6 0 86.36 ${\left| { {\gamma _{ {\rm{HH\text{-}HV} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素 44 0 2 95.65 ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{min} } } }$+超像素 41 3 0 93.18 ${\left| { {\gamma _{ {\rm{(HH-VV) \text{-}(HV)} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素 43 1 3 91.49 多特征融合+超像素 44 0 1 97.78
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