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Extracting Roads from Remote Sensing Images with the Aid of Path Morphology and Parallelized Graph Cuts

机译:借助路径形态学和并行图割从遥感影像中提取道路

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摘要

While Graph Cuts are used for image segmentation, there exist two problems: how to get better initial information of foreground and background and how to improve the executing efficiency of Graph Cuts algorithm. To solve the first problem, path morphology and line segment matching algorithm are performed to get initial background information at the same time as getting initial foreground information, so non-road pixels similar with road pixels in gray value or texture are avoided being segmented as road points. To cope with the second problem, push-relabel strategy is chosen and its parallelized version based on NVIDIA CUDA platform is performed in this paper. Our strategy is performed on dense built-up area and suburban district and proved to be effective in both accuracy and efficiency.
机译:在使用Graph Cuts进行图像分割时,存在两个问题:如何获得更好的前景和背景的初始信息,以及如何提高Graph Cuts算法的执行效率。为了解决第一个问题,通过执行路径形态学和线段匹配算法来获取初始背景信息,同时获得初始前景信息,从而避免将与道路像素相似的非道路像素的灰度值或纹理作为道路分割。点。为了解决第二个问题,本文选择了push-relabel策略,并基于NVIDIA CUDA平台对其进行了并行化处理。我们的策略是在密集的建筑区域和郊区执行的,并被证明在准确性和效率上都是有效的。

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