基于D-S证据理论的多特征融合SAR图像目标识别方法

Recognition method of multi-feature fusion based on D-S evidence theory in SAR image

针对应用单特征SAR图像进行目标识别准确率低的问题,提出了一种将支持向量机(support vector machine,SVM)和D-S证据理论(Dempster-Shafer,D-S)相结合的多特征融合SAR图像目标识别方法。该方法在对SAR图像预处理的基础上,提取目标的纹理、Hu不变矩和峰值特征,并分别以这3类单特征的SVM分类结果作为独立证据,构造基本概率指派,通过D-S证据的组合规则进行融合,并根据分类判决门限给出最终的目标识别结果。将该方法用于SAR图像上的3类目标识别,识别率达95.5%,表明该方法是一种有效的SAR图像目标识别方法。

In view of the low accuracy of the single feature - based method for target recognition in SAR image, a multi - feature decision - making level fusion method based on SVM and D - S evidence theory was proposed. After a series of image processing, the texture feature, Hu invariant moments feature and peek feature were extracted from the target image. Then the targets were classified according to each type of features utilizing SVM, and the results were used as evidence to construct the basic probability assignment. Conclusively, D - S combination rule of evidence was used to achieve fusion, and final recognition results were given by classification thresholds. The method is used for recognizing three - class targets in MSTAR database, and the recognition rate arrives at 95.5%. Experimental result shows that the method is effective for SAR images target recognition.