面向目视解译的全极化SAR船只精细化特征表征方法

Refined Ship Feature Characterization Method of Full-polarimetric Synthetic Aperture Radar for Visual Interpretation

随着卫星技术的发展,极化合成孔径雷达(PolSAR)数据的分辨率和数据质量得到大幅提升,为人造目标的精细化目视解译提供了良好的数据条件。目前主要采用多分量分解的方法,但是易造成像素错分问题,为此,该文结合Yamaguchi极化分解和极化熵提出了一种非固定阈值划分的方法用于实现全极化SAR图像船只结构精细化特征表征。Yamaguchi极化分解能够识别基本散射机制,其修正后的体散射模型更符合实测数据,可有效对人造目标进行表征。极化熵H在弱去极化状态下可以看成某一指定等效点的目标散射机制,能够有效突出船只主散射特征。因此,该文通过将Yamaguchi极化分解算法的非固定三分量与极化熵的低中高熵内嵌,将其分为非固定阈值的九分类成分,从而降低硬阈值处理在阈值边界处受噪声影响产生的类别随机性。并且将二次散射和单次散射均显著的区域称为混合散射(MSM),以更好匹配实验中船只典型结构的散射类型。在此基础上,利用广义相似性参数进一步缩短类内距离,采用改进后的GSP-Wishart分类器进行迭代聚类,旨在通过提高二次散射和混合散射机制以提高不同类型船只可区分度。最后,该文采用中国上海某港口的高分三号全极化SAR数据进行实验,为了验证每艘船只特征表征正确性,通过船舶自动识别系统(AIS)收集并筛选了该港口船只信息及光学数据,并与极化SAR数据中每艘船只进行匹配。实验结果表明该方法可有效区分散货船、集装箱船和油轮3种类型船只。

With advances in satellite technology,Polarimetric Synthetic Aperture Radar(PolSAR)now have higher resolution and better data quality,providing excellent data conditions for the refined visual interpretation of artificial targets.The primary method currently used is a multicomponent decomposition,but this method can result in pixel misdivision problems.Thus,we propose a non-fixed threshold division method for achieving advanced feature ship structure characterization in full-polarimetric SAR images.Yamaguchi decomposition can effectively identify the primary scattering mechanism and characterize artificial targets.Its modified volume scattering model is more consistent with actual data.The polarization entropy can serve as the target scattering mechanism at a specified equivalent point in the weakly depolarized state,which can effectively highlight the ship structure.This paper combines the three components of the Yamaguchi decomposition algorithm with the entropy,and divides it into a nine-classification plane with a non-fixed threshold.This method reduces category randomness generated by noise at the threshold boundary for complicated threshold treatments.Furthermore,the Mixed Scattering Mechanism(MSM)which is the region where both secondary scattering and single scattering are significant,was proposed to better match the scattering types of typical structures of vessels in the experiment.The Generalized Similarity Parameter(GSP)was used to further shorten the intra-class distance and perform iterative clustering using a modified GSP-Wishart classifier.This method improves the vessel distinguishability by enhancing the secondary and mixed scattering mechanisms.Finally,this paper uses full-polarimetric SAR data from a port in Shanghai,China,for the experiment.We collected and filtered ship information and optical data from this port through the Automatic Identification System(AIS)and matched them with the ships in full-polarimetric SAR images to verify the correct characterization of each vessel’s features.The experimental results show that the proposed method can effectively distinguish three types of vessels:bulk carriers,container ships and tankers.