三维模型在SAR图像自动目标识别中的应用

提高高分辨率SAR图像在复杂战场环境中的目标识别能力,对防御未来战争中来自地面目标的威胁具有重要意义。针对地面特定目标的大小、方位、旋转等变化以及强杂波背景给目标识别带来的严重影响,提出将目标的三维模型投影到二维平面,采用余弦傅里叶矩和瑞利分布的CFAR检测方法分别对其矩特征和峰值特征进行提取,利用级联组合分类器对目标识别进行建模分析,并通过试验验证该方法的有效性。结果表明:该方法实现了在特征维数高和姿态变化下的目标识别,而且无需额外增加对制导控制系统的开销。

Application of 3D Model in Automatic Target Recognition of SAR Images

In order to enhance the fast and stable ability of target recognition of high resolution SAR images in a complex battlefield environment. Due to the changing characteristics of size, orientation, rotation, and the severe influence of strong clutter background, that cause serious influence for the ground target recognition. A method of affine 3D object model to two-dimensional plane is proposed, Cosine-Fourier moment and Rayleigh distribution CFAR detection are used to extract the moment and peak features, respectively. The model and analysis of target recognition are done by cascade combination classifier in high dimension and pose change without additional overhead of guidance and control systems. Finally, experimental results prove it accurate and believable.