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An effective method to detect straight lines from high spatial resolution remotely sensed imagery and its applications for runway extraction

机译:从高空间分辨率的直线检测远程感测图像的有效方法及其对跑道提取的应用

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It has always been an important low-level operation to extract edges from images in the fields of computer vision and image procession, in which straight line extraction is typical and representative. Because most man-made spatial objects, e.g. buildings, roads, etc. often take on near straight-line boundaries, extracting straight lines is often the first step to extract these targets. Straight lines can then be looked as the elementary units for other higher level image interpretations. In this paper, a straight line extraction method combining edge detection and depth-first searching on the vector line layer is proposed and applied to extract runways of airports. The steps include: 1) edges are found with the Canny operator and vectorirzed. The reason to use the Canny operator is because it is designed to be an optimal edge detector, which gives very good results on detecting step or slop like edges. It takes as input a grey scale image, and produces as output an image showing the positions of tracked intensity discontinuities. After this operation, we then vectorize the edge points to be a vector layer with edge tracing.2) With the vector-formatted edge lines, the straight line searching can then be carried out. In order to complete this, topology between arcs should be cleaned and rebuilt, which includes the deletion of repetitive, one-node arcs, and splitting on the intersections, etc. 3) Straight lines are detected with the depth-first searching strategy. With the rebuilt topology, we can easily obtain the begin, end nodes of every line. If the distances of its all vertices to the line connecting the begin, end nodes of an arc are less than some pre-defined threshold, it could be looked as a 'straight line' and extracted. Besides, we are certainly only interested in the straight lines with lengths larger than certain threshold, thus a minimum length threshold should be specified to delete these very short lines. In the searching of straight lines, some arcs should be grouped as a single straight line; some un-straight lines should be split to extract its straight parts. The suitable straight lines are outputted to a vector layer after being re-selected and re-grouped, with distinguishing short, long isolating, long not isolating straight lines. With all these steps, we can get the initial straight vector line layer. 4) To these lines with small interspaces but locate on a single straight line, we use a simple but effective connecting step to 'fill' the gaps. Starting from the vector layer and with the operations of broken line connecting and parallel line detection, the main airport runway can be well extracted, which helps us to locate and recognize airports from high spatial remotely sensed imagery.
机译:它始终是从计算机视觉和图像游行领域中的图像中提取边缘的重要低级操作,其中直线提取是典型的和代表性的。因为大多数人造的空间物体,例如,建筑物,道路等经常接近直线边界,提取直线通常是提取这些目标的第一步。然后可以将直线视为用于其他更高级别图像解释的基本单位。在本文中,提出了一种直线提取方法,组合边缘检测和深度首先搜索在向量线层上进行施加,以提取机场的跑道。步骤包括:1)使用罐头操作员和矢量发现边缘。使用Canny Operator的原因是因为它被设计为最佳边缘检测器,这在检测步骤或斜坡上提供了非常好的结果。它需要输入灰度图像,并产生作为输出显示跟踪强度不连续性的位置的图像。在该操作之后,然后将边缘点向与边缘跟踪的矢量层矢量化为与矢量格式边缘线,然后可以执行直线搜索。为了完成这一点,应该清理和重建弧之间的拓扑,包括删除重复,单节点弧和交叉点上的分裂等。3)用深度首先搜索策略检测直线。通过重建拓扑,我们可以轻松获取每一行的开始,结束节点。如果将其所有顶点的距离连接到连接的线路,则弧的终端节点小于一些预定阈值,它可以被视为“直线”并提取。此外,我们肯定只对长度大于某些阈值的直线感兴趣,因此应指定最小长度阈值以删除这些非常短的线路。在直线的搜索中,一些弧应分组为单个直线;应分开一些不直线线以提取其直线零件。在重新选择并重新分组之后,将合适的直线输出到矢量层,以区别短,长的隔离,长度不隔离直线。通过所有这些步骤,我们可以获得初始的直矢量线层。 4)对于带有小型间隙而且定位在单个直线上的这些行,我们使用简单但有效的连接步骤“填充”空白。从向量层开始,随着断线连接的操作和平行线路检测,主要机场跑道可以很好地提取,这有助于我们从高空间遥感图像中找到和识别机场。

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