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Ship Detection From PolSAR Imagery Using the Complete Polarimetric Covariance Difference Matrix

机译:使用完整的极化协方差差矩阵从PolSAR影像中检测船舶

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摘要

In this paper, we proposed a complete polarimetric covariance difference matrix [CP]-based algorithm for ship detection in polarimetric synthetic aperture radar (PolSAR) imagery. To calculate [CP], we first developed a scheme to reflect the polarimetric scattering differences between ship pixel (SP) and its neighboring pixels (ISPs) and, then, dividedly accumulated the amplitude and phase differences between SP and ISPs. Compared to the polarimetric covariance difference matrix [P] developed in our earlier work, [CP] effectively overcomes the drawback of the lack of the phase information in [P]. To demonstrate the effectiveness of the proposed algorithm, we applied the [CP]-based ship detection algorithm to four PolSAR data sets, including one UAVSAR L-band data set with 21 ships, two AIRSAR L-band data sets with 11 and 22 ships, respectively, and one Radarsat-2 C-band data set with 8 ships. Experimental results show that: 1) the proposed algorithm can effectively detect ships with high target-to-clutter ratio (TCR) values and 2) [CP] has a better performance than the traditional polarimetric covariance matrix [C] and [P] on ship detection. To be more specific, the average TCR value of the proposed algorithm (23.86 dB) is 6.07 and 7.47 dB higher than PNFC (i.e., the geometrical perturbation-polarimetric notch filter) and RSC (i.e., the reflection symmetry method), respectively.
机译:在本文中,我们提出了一种完整的基于极化协方差差分矩阵[CP]的算法,用于极化合成孔径雷达(PolSAR)图像中的舰船检测。为了计算[CP],我们首先开发了一种方案,以反映船像素(SP)与相邻像素(ISP)之间的偏振散射差异,然后分别累积SP和ISP之间的幅度和相位差异。与我们早期工作中开发的极化协方差差分矩阵[P]相比,[CP]有效地克服了[P]中缺少相位信息的缺点。为了证明该算法的有效性,我们将基于[CP]的舰船检测算法应用于四个PolSAR数据集,包括一个21艘舰艇的UAVSAR L波段数据集,两个11艘和22舰艇的AIRSAR L波段数据集,以及一个包含8艘船的Radarsat-2 C波段数据集。实验结果表明:1)该算法可以有效地检测出目标杂波比(TCR)值高的船舶; 2)[CP]在传统的极化协方差矩阵[C]和[P]上具有更好的性能。船舶检测。更具体地,所提出的算法的平均TCR值(23.86dB)分别比PNFC(即,几何扰动-偏振陷波滤波器)和RSC(即,反射对称方法)高6.07和7.47dB。

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