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Fusion Recognition Method of Target's SAR Images Based on Modified D-S Evidence Theory

机译:基于改进D-S证据理论的目标SAR图像融合识别方法

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The method of geometric hashing technology can effectively recognize the targets distorted partially. But when the known targets in training data set don't satisfy with the condition of 360 azimuths, the effect of recognition degrades. In this paper, we present two aspects improve the correct rate. Firstly, we use a CFAR detector based on power transformation to segment the SAR images. And also discuss the interval of power transformation' parameter when the data is Rayleigh distribution. Secondly, we present a method of modified D-S evidence theory which can solve fusion with a high degree of conflict. Experimental results with MSTAR dataset show that this fusion method is effective and feasible
机译:几何哈希技术可以有效地识别出部分变形的目标。但是,当训练数据集中的已知目标不满足360方位角的条件时,识别效果就会降低。在本文中,我们从两个方面提出了提高正确率的方法。首先,我们使用基于功率变换的CFAR检测器来分割SAR图像。并讨论了数据为瑞利分布时功率变换的间隔参数。其次,我们提出了一种改进的D-S证据理论,可以解决高度冲突的融合方法。 MSTAR数据集的实验结果表明,该融合方法是有效可行的。

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