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Polarimetric Synthetic Aperture Radar Interpretation and Recognition: Advances and Perspectives
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摘要:
极化合成孔径雷达(SAR)能够获取目标的全极化信息，在对地观测、灾害评估、侦察监视等民用和军用领域得到广泛应用。国内主要高校、中科院、工业部门和用户单位在该领域开展了卓有成效的工作，取得一大批标志性研究成果。该文简要综述了极化SAR成像解译识别领域的主要研究进展。在解译层面，主要介绍了极化目标分解和极化旋转域解译等理论方法的研究进展。在应用层面，结合研究团队的工作，探讨了上述理论方法在舰船检测、地物分类和建筑物损毁评估等领域的应用成效。最后，对极化SAR目标解译识别技术的研究进行了展望。
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关键词:
- 极化合成孔径雷达(极化SAR) /
- 极化目标分解 /
- 极化旋转域 /
- 散射机理 /
- 目标检测、分类和识别 /
- 灾害评估
Abstract:Polarimetric Synthetic Aperture Radar (SAR), which can acquire fully polarimetric information, is widely used in civilian and military fields, such as earth observation, damage assessment, and reconnaissance. Major Chinese universities, the Chinese Academy of Sciences, the industrial sector, and user units have conducted research in this field and obtained numerous remarkable achievements. This work reviews the recent progress of research in the field of polarimetric SAR imaging interpretation and recognition. For target scattering interpretation, theories of polarimetric target decomposition and polarimetric rotation domain interpretation are introduced. For polarimetric SAR application, the technologies of ship detection, land cover classification, and building damage assessment, which are based on the interpretation tools, are summarized in combination with the authors’ own research. Finally, the future development perspectives of polarimetric SAR interpretation and recognition are briefly discussed.
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图 3 极化旋转域可视化表征
Figure 3. Visualization and characterization in polarimetric rotation domain
图 4 极化相关方向图特征的TCR对比结果
Figure 4. TCR comparisons in terms of polarimetric correlation pattern features
图 7 相干斑滤波处理后的多时相UAVSAR数据及真值图
Figure 7. Multi-temporal UAVSAR datasets with speckle reduction and the ground-truth data
图 8 建筑物倒损前后极化散射机理变化示意图
Figure 8. Illustration of the changes of the polarimetric scattering mechanisms
图 9 广域建筑物倒损率估计及与真值数据的对比(2011.03.11东日本大地震)
Figure 9. Urban damage mapping results and comparisons with the ground-truth data (the March 11th, 2011, East Japan earthquake)
表 1 高分三号极化SAR舰船目标检测结果
Table 1. Ship detection results with GaoFen-3 data
方法 ${N_{\rm{C}}}$ ${N_{\rm{M}}}$ ${N_{{\rm{FA}}}}$ FoM(%) SO-CFAR方法 181 61 0 74.79 Saliency方法 225 17 0 92.98 ${\left| { { {\hat \gamma }_{ {\rm{HH {\text{-}} HV} } } }\left( \theta \right)} \right|_{ {\rm{org} } } }$ 236 6 3 96.33 ${\left| { { {\hat \gamma }_{({\rm{HH - VV} }){\rm{ {\text{-} } (HV)} } } }\left( \theta \right)} \right|_{ {\rm{min} } } }$ 235 7 2 96.31 ${\left| { { {\hat \gamma }_{({\rm{HH - VV} }){\rm{ {\text{-} } (HV)} } } }\left( \theta \right)} \right|_{ {\rm{org} } } }$ 237 5 2 97.13 
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表 2 UAVSAR极化SAR地物分类的总体分类精度(%)
Table 2. The OAs of polarimetric SAR land cover classification results with UAVSAR data (%)
DoY Method Training ratio 10% 5% 1% 169 ${T_3}$+CNN 99.35 99.38 98.92 SF+CNN 99.54 99.43 98.86 174 ${T_3}$+CNN 93.18 93.70 92.37 SF+CNN 98.69 98.11 97.04 175 ${T_3}$+CNN 99.53 99.43 98.87 SF+CNN 99.42 99.23 98.34
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