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SHIP DETECTION APPROACH BASED ON CROSSCORRELATION FROM DUAL-POLARIZATION DATA

机译:基于双极化数据横相关的船舶检测方法

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Preliminary results are reported on ship detection using coherence images computed from crosscorrelating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, crosscorrelation of multi-look images yields the different degrees of coherence between the images and water. The polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV. In the next step, we examine the technique when the dual-polarization data are split into two multi-look images.
机译:使用从Envisat Asar的多外表处理的双极化数据(HH和HV)的跨相关图像计算的相干图像来报告船舶检测的初步结果。诸如CFAR(恒定假报警速率)的雷达的传统船舶检测技术依赖于幅度数据,因此当船舶图像的幅度处于与周围海洋杂波的平均幅度类似的水平时,检测趋于变得困难。所提出的方法利用船舶的多页图像彼此相关的特性。因为海面的外观图像被不相关的斑点覆盖,所以多看图像的横相关产生图像和水之间的不同程度。首先基于HH和HV之间的互相关来比较船只,陆地和透模区的偏振信息。在下一步中,当双极化数据被分成两个多样图像时,我们检查该技术。

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