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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Merchant Vessel Classification Based on Scattering Component Analysis for COSMO-SkyMed SAR Images
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Merchant Vessel Classification Based on Scattering Component Analysis for COSMO-SkyMed SAR Images

机译：基于散射成分分析的COSMO-SkyMed SAR商船分类

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摘要

Ship classification in high-resolution synthetic aperture radar (SAR) satellite images is a hotspot and a continuing problem in SAR applications. The scattering components of ships are the strong scatter of objects in SAR images, and these can represent the superstructure of different ship types. Based on analyses of different scattering components of bulk carriers, oil tankers, and container ships, we propose a new classification method for these three ship types in COSMO-SkyMed SAR images. First, morphological preprocessing is applied to suppress sidelobes. Second, based on Hough transform (HT), the orientation of the principal axis is extracted, and the modified minimum enclosing rectangle (MER) of the ship is obtained and rotated along the principal axis. Finally, the ship type is decided according to the width ratio of MER between the HT line, the ratio of ship and nonship points on the principal axis, and the scattering density. The results show that this method has good performance in ship classification, with an overall accuracy of over 80%.
机译：高分辨率合成孔径雷达（SAR）卫星图像中的船舶分类是一个热点，并且是SAR应用中的持续问题。船舶的散射成分是SAR图像中物体的强烈散射，可以代表不同类型船舶的上层建筑。在分析散货船，油轮和集装箱船的不同散射成分的基础上，我们提出了在COSMO-SkyMed SAR图像中针对这三种船型的新分类方法。首先，应用形态学预处理来抑制旁瓣。其次，基于霍夫变换（HT），提取主轴的方向，并获得修改后的船舶最小包围矩形（MER），并沿主轴旋转。最后，根据HT线之间的MER的宽度比，主轴上的船舶和非船舶点的比率以及散射密度来确定船的类型。结果表明，该方法在船舶分类中具有良好的性能，总体精度超过80％。

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