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SAR amplitude filtering using TV prior and its application to build-ing delineation

机译:使用TV先验的SAR幅度滤波及其在建筑物轮廓描绘中的应用

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This paper investigates the use of a popular regularization model, the Total Variation minimization (TV), to filter SARimages and reduce speckle noise. This model is extensively used for its property of preserving edges. Due to the manylocal minima, TV minimization is difficult to achieve for non-convex likelihood terms such as that of SAR amplitude.Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Exact minimization,although theoretically possible, can not be implemented due to memory constraints on large images required by remotesensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization is alsoheavy. In this paper, we propose a new fast approximate discrete algorithm. The filtering is applied in the framework ofbuilding delineation for 3D reconstruction. Results on real images are presented.
机译:本文研究了使用流行的正则化模型总变化最小化(TV)来过滤SAR 图像并减少斑点噪声。该模型因其保留边缘的特性而被广泛使用。由于很多 对于局部最小值,对于非凸似然项(例如SAR幅度),很难实现TV的最小化。 这样的最小化可以通过计算加权图的最小割来有效地执行。精确最小化 尽管理论上可行,但由于远程需要的大图像上的内存限制而无法实现 传感应用。近似最小化的最新算法的计算负担也是 重的。在本文中,我们提出了一种新的快速近似离散算法。过滤应用于以下框架 为3D重建绘制建筑物轮廓。呈现了真实图像上的结果。

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