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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Superpixel-Level Target Discrimination for High-Resolution SAR Images in Complex Scenes
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Superpixel-Level Target Discrimination for High-Resolution SAR Images in Complex Scenes

机译：复杂场景中高分辨率SAR图像的超像素级目标识别

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摘要

Traditional synthetic aperture radar (SAR) target discrimination methods are implemented at the chip-level, which may have good discrimination performance in simple scenes but may lose the effectiveness in complex scenes. To improve the discrimination performance in complex scenes, this paper proposes a superpixel-level target discrimination method directly in high-resolution SAR images. The proposed discrimination method mainly contains three stages. First, based on the superpixel-level target detection results, we describe each superpixel via the multilevel and multidomain feature descriptor, which can reflect the differences between targets and clutter comprehensively. Second, we employ the support vector machine as the discriminator to obtain the discriminated target superpixels. Finally, we cluster the discriminated target superpixels and extract the target chips from the original SAR image based on the clustering results. The experimental results based on the miniSAR real SAR data show that the proposed discrimination method has about 25% higher F1-score than the traditional discrimination methods.
机译：传统的合成孔径雷达（SAR）目标识别方法是在芯片级实现的，在简单场景中可能具有良好的识别性能，但在复杂场景中可能会失去有效性。为了提高复杂场景下的判别性能，本文提出了一种直接用于高分辨率SAR图像的超像素级目标判别方法。所提出的判别方法主要包括三个阶段。首先，基于超像素级目标检测结果，我们通过多级和多域特征描述符描述每个超像素，可以全面反映目标之间的差异和混乱。其次，我们采用支持向量机作为判别器，以获得被判别的目标超像素。最后，将聚类的目标超像素聚类，并根据聚类结果从原始SAR图像中提取目标芯片。基于miniSAR真实SAR数据的实验结果表明，所提出的判别方法比传统判别方法具有更高的F1分数。

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