高分辨合成孔径雷达图像的耦合传统恒虚警目标检测

doi:10.13229/j.cnki.jdxbgxb20170984

摘要/Abstract

摘要：

由于合成孔径雷达(SAR)图像中含有大量相干斑噪声导致目标特征不太明显,仅使用目标强散射特征的传统恒虚警(CFAR)算法时虚警率较高。针对该问题,本文提出了一种新的耦合CFAR检测算法,它既能够检测目标的强散射特征,又能够检测目标的阴影特征。首先,利用目标强散射特征与阴影特征之间的协同关系进行目标检测。耦合CFAR的强散射特征检测与传统CFAR算法完全相同,对阴影特征检测则通过修改传统CFAR算法的积分区间来实现。然后,根据同一个目标的强散射特征与阴影特征之间存在的空间位置关联关系删除虚警目标,取得了更好的检测效果。试验结果表明,该算法在相同检测率的情况下,其虚警率远低于传统CFAR算法。

关键词: 信息处理技术, 目标检测, 图像语义, 高分辨率, 合成孔径雷达图像

Abstract:

The speckle noise causes the target feature unobvious in the SAR image. The traditional CFAR algorithm only considers the strong scattering feature of object, and it is easily affected by much false alarm target. This paper proposes a novel conjugate CFAR algorithm which is more precise for object detection. The conjugate CFAR algorithm extracts the strong scattering feature as the traditional CFAR algorithm does. Then, the shadow feature detection is implemented by modifying the integral interval of the traditional CFAR algorithm. Therefore, the better results can be achieved by deleting the false alarm target according to the spatial relationship between the strong scattering feature and the shadow feature. The experimental results show that the false alarm rate of the algorithm is much lower than that of the traditional CFAR algorithm at the same detection rate.

Key words: information procession technology, object detecting, image semantic, very high resolution, synthetic aperture radar(SAR) image

中图分类号:

TP391.4

黄勇,杨德运,乔赛,慕振国. 高分辨合成孔径雷达图像的耦合传统恒虚警目标检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1904-1909.

HUANG Yong,YANG De-yun,QIAO Sai,MU Zhen-guo. Target detecting with conjugate CFAR in VHR SAR image[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1904-1909.

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