基于阴影信息的多视角SAR图像识别

传统的SAR图像识别技术主要基于目标的电磁散射特性，而目标阴影信息对SAR图像目标识别具有重要的作用。若能获取同一目标在多个方位角下的多幅SAR图像，可改善目标识别的性能。针对该问题，本文提出了一种基于隐马尔可夫模型及阴影信息的多视角SAR图像识别技术。该技术提取目标阴影形状的链编码作为特征向量，并结合同一目标在不同方位角下的多幅图像的特征向量，生成该目标的特征序列，然后利用HMM对特征序列进行识别。仿真结果表明，该方法可有效实现SAR图像目标识别。

SAR IMAGE RECOGNITION BASED ON MULTI-ASPECT OF SHADOW INFORMATION

The traditional synthetic aperture radar (SAR) image recognition techniques focus on the electro mag-netic (EM) scattering centers, ignoring the important role of the shadow information on the SAR image recogni-tion.It is difficult to classify targets by the shadow information independently, because the shadow shape is de-pendent on the radar aspect angle, the depression angle and the resolution.Moreover, the shadow shapes of dif-ferent targets are similar.When the multiple SAR images of one target from different aspects are available, the performance of the target recognition can be improved.Aimed at the problem, a multi-aspect SAR image recogni-tion technique based on the shadow information is developed.It extracts shadow profiles from SAR images, and takes chain codes as the feature vectors of targets.Then, feature vectors on multiple aspects of the same target are combined with feature sequences, and the hidden Markov model (HMM) is applied to the feature sequences for the target recognition.The simulation result shows the effectiveness of the method.