首页> 外文会议>Conference on Remotely Sensed Data and Information; 20070525-27; Nanjing(CN) >An effective method to detect straight lines from high spatial resolution remotely sensed imagery and its applications for runway extraction
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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:一种从高空间分辨率遥感影像中检测直线的有效方法及其在跑道提取中的应用

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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:在计算机视觉和图像处理领域中,从图像中提取边缘一直是重要的低级操作,其中直线提取是典型且具有代表性的。因为大多数人造空间物体,例如建筑物,道路等通常采用接近直线的边界,提取直线通常是提取这些目标的第一步。然后,可以将直线视为其他更高级别图像解释的基本单位。提出了一种结合边缘检测和深度优先搜索的矢量线层直线提取方法,并将其应用于机场跑道的提取。步骤包括:1)使用Canny运算符找到边缘并将其矢量化。之所以使用Canny运算符,是因为它被设计为最佳的边缘检测器,在检测台阶或类似坡度的边缘时可以提供非常好的结果。它以灰度图像作为输入,并生成显示跟踪的强度不连续位置的图像作为输出。在此操作之后,我们将边缘点矢量化为具有边缘追踪的矢量层。2)使用矢量格式的边缘线,然后可以执行直线搜索。为此,应清理并重建弧之间的拓扑,包括删除重复的,单节点弧以及在相交处拆分等。3)使用深度优先搜索策略检测直线。使用重建的拓扑,我们可以轻松获得每行的开始,结束节点。如果其所有顶点到连接弧的起点,终点的线的距离小于某个预定阈值,则可以将其视为“直线”并提取。此外,我们当然只对长度大于特定阈值的直线感兴趣,因此应指定最小长度阈值以删除这些非常短的线。在搜索直线时,应将某些弧线归为一条直线。一些非直线应该分开以提取其直线部分。在重新选择并重新分组后,将合适的直线输出到矢量层,并区分短,长隔离,长不隔离直线。通过所有这些步骤,我们可以获得初始直线向量线层。 4)对于这些行距较小但位于一条直线上的线,我们使用简单但有效的连接步骤来“填充”间隙。从矢量层开始,通过虚线连接和平行线检测的操作,可以很好地提取主要机场跑道,这有助于我们从高空间遥感影像中定位和识别机场。

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