Anisotropic Third-Order Regularization for Sparse Digital Elevation Models
Anisotropic Third-Order Regularization for Sparse Digital Elevation Models
复制标题
稀疏数字高程模型的各向异性三阶正则化
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
C. Schönlieb
中科院分区：
文献类型：
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作者：
J. Lellmann;J. Morel;C. Schönlieb
我们考虑了基于稀疏数据（如单个点或水平线）插值曲面的问题。我们推导出满足一系列理想属性的插值器，重点是保留轮廓的几何形状和特征特征，同时确保水平线之间的平滑。我们提出了一种各向异性三阶模型和一种有效的自适应估计表面和各向异性的方法。实验表明，该方法在实际数字高程数据的定性和定量上都优于AMLE和高阶总变分方法。
We consider the problem of interpolating a surface based on sparse data such as individual points or level lines. We derive interpolators satisfying a list of desirable properties with an emphasis on preserving the geometry and characteristic features of the contours while ensuring smoothness across level lines. We propose an anisotropic third-order model and an efficient method to adaptively estimate both the surface and the anisotropy. Our experiments show that the approach outperforms AMLE and higher-order total variation methods qualitatively and quantitatively on real-world digital elevation data.