Semi automatic road extraction from digital images
Semi automatic road extraction from digital images
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DOI：
10.1016/j.ejrs.2017.03.001
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
H. Bakhtiari;A. Abdollahi;Hani Rezaeian
中科院分区：
文献类型：
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作者：
H. Bakhtiari;A. Abdollahi;Hani Rezaeian
从数字图像中提取道路对于自动制图、有效的城市规划和更新GIS数据库具有重要意义。机载和星载传感器获取的极高空间分辨率（VHR）图像是精确道路提取的主要来源。手工技术正在逐渐消失，因为它们既耗时又昂贵。因此，自动化程度更高的道路提取方法已成为遥感信息处理的研究热点。提出了一种从高分辨率遥感影像中半自动提取不同道路类型的方法。该方法是基于边缘检测和支持向量机和数学形态学方法。首先利用Canny算子检测出道路的轮廓;然后，全Lambda调度合并方法合并相邻段。然后利用支持向量机（SVM）和各种空间、光谱和纹理属性对整幅图像进行分类，形成道路图像。最后，利用形态学算子提高了道路检测的质量。该算法进行了系统的评估，从世界观，QuickBird和UltraCam机载图像的各种卫星图像。精度评估结果表明，所提出的道路提取方法可以提供高精度的提取不同的道路类型。
Road extraction from digital images is of fundamental importance in the context of automatic mapping, effective urban planning and updating GIS databases. Very high spatial resolution (VHR) imagery acquired by airborne and space borne sensors is the main source for accurate road extraction. Manual techniques are fading away as they are time consuming and costly. Hence, road extraction method that is significantly more automated has become a research hotspot in remote sensing information processing. This paper proposes a semi-automatic approach to extract different road types from high-resolution remote sensing images. The approach is based on edge detection and SVM and mathematical morphology method. First the outline of the road is detected based on Canny operator. Then, Full Lambda Schedule merging method combines adjacent segments. Then the entire image was classified using Support Vector Machine (SVM) and various spatial, spectral, and texture attributes to form a road image. Finally, the quality of detected roads is improved using morphological operators. The algorithm was systematically evaluated on a variety of satellite images from Worldview, QuickBird and UltraCam airborne Images. The results of the accuracy evaluation demonstrate that the proposed road extraction approach can provide high accuracy for extraction of different road types.