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Ship Detection via Superpixel-Random Forest Method in High-Resolution SAR Images

机译:在高分辨率SAR图像中通过Superpixel-Walant Forest方法进行船舶检测

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摘要

With the increasing resolution of synthetic aperture radar (SAR), the traditional SAR image target detection methods used for medium-low resolution are not suitable for high-resolution SAR images, which contain detailed information about structure, shape, and weak echoes that are hardly detected in traditional ways. In this paper, we proposed a new method, Superpixel-Random Forest Technique, to detect ships in high-resolution SAR images. The method combines superpixel and random forest algorithms. The superpixel is adopted to divide images into many subregions properly, and the random forest is used for unsupervised clustering these subregions into ships or others. The experimental results show that the algorithm can accurately detect the ship targets.
机译:随着合成孔径雷达(SAR)的越来越多的分辨率,用于中低分辨率的传统SAR图像目标检测方法不适用于高分辨率SAR图像,其包含有关结构,形状和弱回声的详细信息以传统方式检测到。在本文中,我们提出了一种新的方法,超像素随机林技术,以检测高分辨率SAR图像中的船舶。该方法结合了Superpixel和随机林算法。采用SuperPixel正确地将图像分成许多次区域,随机森林用于无监督的聚类这些次区域进入船舶或其他子区域。实验结果表明,该算法可以准确地检测船舶目标。

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