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Runway detection using line segment statistical model

机译:使用线段统计模型进行跑道检测

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摘要

To meet the needs of precise runway detection in remote sensing images, while reducing unnecessary adjustable parameters, an efficient and parameter free method is proposed to extract the straight edge of runway based on Helmholtz principle. By introducing the model of line segment model, we compute and analyze the gradient of the pixels on a given oriented line, and then the problem of line segment is classified into hypothesis test under tolerant significance. In theory, this proposed method can limit the number of false alarms and turns out to be parameterless. Using the end point of detected line segment, the extraction of parallel line is of light computational burden. The results of concrete surface and pitch surface airport show our method avoid initializing the type of runway, and is not sensitive to noise and the gray-level difference between airport and ground. The final allocation of runway in an image is accurate and the turning around time is greatly condensed.
机译:为了满足遥感影像中精确跑道检测的需要,在减少不必要的可调参数的同时,提出了一种基于亥姆霍兹原理的高效,无参数的跑道直边提取方法。通过引入线段模型的模型,我们计算并分析了给定方向线上的像素的梯度,然后将线段的问题归类为假设检验。从理论上讲,该方法可以限制错误警报的数量,并且证明是无参数的。使用检测到的线段的端点,平行线的提取具有轻的计算负担。混凝土地面和沥青地面机场的结果表明,我们的方法避免了初始化跑道的类型,并且对噪声和机场与地面之间的灰度级差异不敏感。图像中跑道的最终分配是准确的,并且转弯时间大大缩短了。

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