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A novel parallel algorithm for airport runway segmentation in satellite images using priority directional region growing strategy based on ensemble learning

机译：基于集成学习的优先方向区域增长策略的卫星图像机场跑道分割并行算法

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摘要

This paper addresses the problem of airport runway segmentation in satellite images with complex background clutter. To this ends, we propose a novel ensemble learning based parallel runway segmentation algorithm. The contributions of our work can be summarized as follows: (a) we propose the concept of Priority Directional Region Growing, (b) We introduce the Bresenham's line generating algorithm into our segmentation task to better utilize the structural a priori, (c) we adopt a two-stage strategy to better segment the regions corresponding to the airport runway by applying the traditional region growing method and our priority directional (two orthogonal directions in our problem) region growing method sequentially, (d) In our runway segmentation algorithm, the ensemble-learning strategy is used to combine the growing results of each detected line segment. In addition, those thin side branches, which have significantly different width, are eliminated. To evaluate the effectiveness of our algorithm, extensive simulations are carried out on the testing images obtained from Google Map. Our experimental results show that the proposed algorithm can effectively and efficiently segmented the airport region, generate relatively neat boundaries of the runways, and have great superiority over the state-of-the-art-methods.
机译：本文解决了具有复杂背景杂波的卫星图像中的机场跑道分割问题。为此，我们提出了一种新颖的基于集合学习的并行跑道分割算法。我们的工作可以概括如下：（a）我们提出了优先定向区域增长的概念，（b）将布雷森汉姆的线生成算法引入到我们的分割任务中，以更好地利用结构先验，（c）采取两阶段策略，通过依次应用传统的区域增长方法和我们的优先方向（在我们的问题中为两个正交方向）区域增长方法，更好地分割与机场跑道相对应的区域；（d）在我们的跑道分割算法中，集成学习策略用于组合每个检测到的线段的增长结果。另外，消除了宽度明显不同的那些细的侧分支。为了评估我们算法的有效性，对从Google Map获得的测试图像进​​行了广泛的模拟。我们的实验结果表明，所提出的算法可以有效，高效地分割机场区域，生成相对整齐的跑道边界，并且比现有的方法具有更大的优势。

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