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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ChanVese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。
Purpose Synthetic aperture radar (SAR) makes the collected data sparse and incomplete due to the imaging method, geometric angle and other reasons, and can not restore the object truly if it is modeled directly. Aimed at the characteristics of down-looking SAR data, a reconstruction method that can automatically repair sparse and incomplete data during modeling is proposed. Methods Firstly, the Otsu method was used to pre-process the 3-D SAR data. Then the ChanVese model in the 2-D image segmentation method was extended to the surface reconstruction of the down-looking SAR data. During the initial surface and contour indicator function were introduced Distance function and inner product function. Results Compared with the reconstruction results of isosurface extraction, the proposed method can automatically repair the holes in the process of reconstruction, and the reconstructed model has a smoother surface that can more accurately reflect the characteristics of the original objects. Conclusion This method can be extended to the modeling of sparse and incomplete SAR data.