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基于精细模型分解和保护滤波器的聚束式 SAR 图像船舶检测融合方法。

Integration of Fine Model-Based Decomposition and Guard Filter for Ship Detection in PolSAR Images.

机构信息

School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China.

PIESAT Information Technology Co., Ltd., Beijing 100195, China.

出版信息

Sensors (Basel). 2021 Jun 23;21(13):4295. doi: 10.3390/s21134295.

Abstract

Ship detection with polarimetric synthetic aperture radar (PolSAR) has gained extensive attention due to its widespread application in maritime surveillance. Nevertheless, designing identifiable features to realize accurate ship detection is still challenging. For this purpose, a fine eight-component model-based decomposition scheme is first presented by incorporating four advanced physical scattering models, thus accurately describing the dominant and local structure scattering of ships. Through analyzing the exclusive scattering mechanisms of ships, a discriminative ship detection feature is then constructed from the derived contributions of eight kinds of scattering components. Combined with a spatial information-based guard filter, the efficacy of the feature is further amplified and thus a ship detector is proposed which fulfills the final ship detection. Several qualitative and quantitative experiments are conducted on real PolSAR data and the results demonstrate that the proposed method reaches the highest figure-of-merit (FoM) factor of 0.96, which outperforms the comparative methods in ship detection.

摘要

由于在海上监视中得到广泛应用,极化合成孔径雷达(PolSAR)的船舶检测技术受到了广泛关注。然而,设计可识别的特征以实现准确的船舶检测仍然具有挑战性。为此,首先提出了一种精细的基于八分量模型的分解方案,该方案结合了四种先进的物理散射模型,从而可以准确描述船舶的主要和局部结构散射。通过分析船舶的独特散射机制,然后从八种散射分量的贡献中构建出具有判别力的船舶检测特征。结合基于空间信息的保护滤波器,进一步放大该特征的有效性,从而提出了一种满足最终船舶检测的船舶探测器。在真实的 PolSAR 数据上进行了一些定性和定量实验,结果表明,所提出的方法达到了 0.96 的最高品质因数(FoM)因子,在船舶检测方面优于比较方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5138/8271865/81e208d6aa1e/sensors-21-04295-g001.jpg

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