随着合成孔径雷达(SAR)技术的发展,SAR获取的数据量迅速增加,利用SAR图像对目标进行识别时,计算量大、耗时长。为了快速、有效地识别目标,提出了一种基于几何模型的目标快速分类方法。该方法选择二值目标区域和阴影区域为特征,首先利用目标几何模型的光学可见信息进行正向特征预测。然后将实测SAR图像中提取的二值区域与预测得到的二值区域对齐,建立相关性。最后通过融合相似度准则进行判断,实现目标分类,并在MSTAR数据集上验证了该方法的高效性和有效性。由于该方法不涉及耗时的电磁计算,在减小计算量的同时,加快了目标识别速度。

With the development of synthetic aperture radar (SAR) technology, the amount of data acquired by SAR increases rapidly. When SAR images are used to identify targets, the amount of calculation is large and time-consuming. In order to realize fast and effective recognition of targets, we propose a fast classification method of targets based on geometric model. In this method, binary target region and shadow region are selected as features. First, the forward features are predicted by using the optical visible information of the target geometry model. Then, the binary region extracted from the measured SAR images is aligned with the predicted binary region to establish the correlation. Finally, by judging the similarity criterion, the target classification is realized, and the efficiency and validity of the method are verified on MSTAR data set. Since this method does not involve time-consuming electromagnetic calculation, it can reduce the amount of calculation and accelerate the speed of target recognition.