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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Spatially Regularized Fusion of Multiresolution Digital Surface Models
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Spatially Regularized Fusion of Multiresolution Digital Surface Models

机译：多分辨率数字表面模型的空间正则化融合

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摘要

In this paper, we propose an algorithm for robustly fusing digital surface models (DSMs) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. Robust fusion of the DSMs is achieved by minimizing the L1-distance of each pixel of the solution to each input DSM. This approach is similar to a pixel-wise median, and most outliers are discarded. We further incorporate local planarity assumption as an additional constraint to the optimization problem, thus reducing the noise compared with pixel-wise approaches. The optimization is also inherently able to include weights for the input data, therefore allowing to easily integrate invalid areas, fuse multiresolution DSMs, and to weight the input data. The complete optimization problem is constructed as a variational optimization problem with a convex energy functional, such that the solution is guaranteed to converge toward the global energy minimum. An efficient solver is presented to solve the optimization in reasonable time, e.g., running in real time on standard computer vision camera images. The accuracy of the algorithms and the quality of the resulting fused surface models are evaluated using synthetic data sets and spaceborne data sets from different optical satellite sensors.
机译：在本文中，我们提出了一种算法，用于使用明确的先验表面先验获得具有局部光滑表面的模型，以可靠地融合具有不同地面采样距离和置信度的数字表面模型（DSM）。通过最小化解决方案的每个像素到每个输入DSM的L1距离，可以实现DSM的鲁棒融合。这种方法类似于像素级中值，并且大多数异常值都被丢弃。我们进一步将局部平面性假设作为对优化问题的附加约束，从而与逐像素方法相比降低了噪声。该优化还固有地能够包含输入数据的权重，因此可以轻松集成无效区域，融合多分辨率DSM并加权输入数据。完整的优化问题被构造为具有凸能量函数的变分优化问题，从而确保解可以收敛到全局能量最小值。提出了一种有效的求解器以在合理的时间内解决优化问题，例如在标准计算机视觉相机图像上实时运行。使用来自不同光学卫星传感器的合成数据集和星载数据集来评估算法的准确性和生成的融合曲面模型的质量。

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