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Road identification in aerial images on fractional differential and one-pass ridge edge detection

机译:基于分数差分和一过山脊边缘检测的航拍图像道路识别

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摘要

A method for detecting and identifying thin and vague roads in aerial images is proposed. The method first smooths an image by using a Gaussian filter, then uses a fractional differential operator to enhance the image for sharpening roads, then applies a one-pass ridge edge detection algorithm to roughly detect the roads, and finally utilizes a number of post functions to accurately identify roads. For each detected point in an aerial road image, the new ridge detection algorithm detects if it is a candidate for the ridge edge points by searching through four different directions. After that, the extracted segments of lines/curves are smoothed and their gaps are linked according to preset thresholds of lengths and directions, and the noisy lines are removed based on the rules of the curve length and shape information. If roads are thick, the image can be shrunk to a road with a width less than six pixels, then the detection result for the course resolution image is mapped into the original image to accurately re-identify the road. In experiments, by comparison to traditional methods, the studied method can have better detection results for thin and vague roads, which are difficult to detect with traditional algorithms. c The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
机译:提出了一种在航空影像中检测和识别薄弱路段的方法。该方法首先通过使用高斯滤波器对图像进行平滑处理,然后使用分数微分算子对图像进行锐化以增强图像的清晰度,然后应用一次通过的脊边缘检测算法粗略地检测道路,最后利用许多后置函数准确识别道路。对于空中道路图像中的每个检测到的点,新的山脊检测算法通过搜索四个不同方向来检测它是否是山脊边缘点的候选对象。然后,对提取的线段/曲线段进行平滑处理,并根据预设的长度和方向阈值将其间隙链接起来,并根据曲线长度和形状信息的规则去除噪点。如果道路较粗,可以将图像缩小为宽度小于六个像素的道路,然后将路线分辨率图像的检测结果映射到原始图像中,以准确地重新识别道路。在实验中,与传统方法相比,所研究的方法在较薄且模糊的道路上具有更好的检测结果,而传统方法则难以检测。 c作者。由SPIE根据Creative Commons Attribution 3.0 Unported License发布。分发或复制此作品的全部或部分,需要对原始出版物(包括其DOI)进行完全归因。

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