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中文摘要：
当前对空间目标的几何匹配研究主要侧重干线目标和面目标匹配，对空间点群目标的相似匹配研究较少，主要采用位置差异度和结构相似度来进行匹配，匹配的成功率不高且易受噪声影响。本文提出一种基于KL特征的空间点群相似匹配算法，首先提取点群的基本关系特征，在此基础上进行点群的KL特征提取，然后再进行点群匹配。实验表明：对等距变换而言，基于距离的KL特征提取的匹配方法最为有效，且不易受到噪声影响。
英文摘要：
Currently geometric matching mainly focuses on the line and polygon targets matching. While the research on point group target matching is few, and it often uses position difference degree and structural similarity for matching, therefore the matching success rate is not high and susceptible to noise. This paper proposes a method of point group similarity matching algo-rithm based on the characteristics of KL. Firstly, the basic relationship features of point group are extracted, and then the KL fea-ture is extracted, finally the point group matching is conducted. The experimental results show that the matching method of KL features extracting based on distance is most effective for isometries, and less susceptible to noise.
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