基于K-means和Hough变换的城市航拍图像道路自动数字化

2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) Pub Date : 2018-08-01 DOI:10.1109/ICCCEEE.2018.8515861

Mohammed Nasser

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引用次数: 0

摘要

在地理信息系统(GIS)中，数字化是一个费时费力的过程，用于数字制图和地图更新。移动服务和基于道路网络的自动驾驶汽车技术的普及增加了道路自动数字化的需求。近年来，深度学习在航空和卫星图像的道路分割上取得了优异的成绩[1]，突出了自动数字化的特点。本文采用统计方法从分割后的图像中提取道路，并对三种不同场景的图像进行了测试，平均准确率为86%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automatic Road Digitizing of Segmented Aerial Images for Urban Areas Based on K-means and Hough Transformation

In Geographical Information System (GIS), digitizing is an exhausting and time consuming process which is used for digital mapping and map updating. Popularity of mobile services and autonomous vehicles technology based on roads network increase the need for road automatic digitizing. Last years, deep-learning gave excellent results on roads segmentation from aerial and satellite imagery [1], that highlighted the automatic digitizing. In this paper statistical method is applied to extract roads from segmented images, and tested with images for three different scenes and gave an average accuracy of 86 percent.

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2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)

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