目的 合成孔径雷达(SAR)因成像方法、几何角度等原因使得采集到的数据具有稀疏性及残缺性,如果直接用其进行建模,不能真实地还原物体。针对下视SAR数据的特点,提出一种在建模过程中能够自动修补稀疏及残缺数据的重建方法。方法 首先引入大津法对3维SAR数据进行预处理,然后将2维图像分割方法中的Chan-Vese模型推广应用到下视SAR数据的表面重建中,在初始表面及轮廓指示函数的求取过程中引入距离函数和内积函数。结果 将本文方法与等值面抽取法的重建结果进行比较,本文方法在重建的过程中能够自动修补空洞,重建出的模型表面更加光滑,能更加真实地反映原物体的特征。结论 可以将本文方法推广应用到稀疏及残缺SAR数据的建模中。

Objective The data collected by synthetic aperture radar(SAR) are sparse and incomplete because of the imaging method and geometry angle, which result in difficulties in 3D SAR data surface reconstruction. For downward-looking SAR data, this paper presents a reconstruction method that can repair sparse and incomplete data automatically. Method First, the Otsu method is introduced for the preprocessing of 3D SAR data. Second, the Chan-Vese model of 2D image segmentation method is applied to the surface reconstruction of the downward-looking SAR data. The distance and inner product functions are employed as the initial surface and contour indicator function. Result Compared with the isosurface extraction method, the proposed method can repair the holes automatically during the reconstruction procedure. The reconstructed model surface is smoother and can reflect the characteristics of the original object. Conclusion The proposed method can be applied to the modeling of sparse and incomplete data.