基于亮度补偿的遥感影像阴影遮挡道路提取方法

Extraction of Shaded Roads in High-Resolution Remote Sensing Imagery based on Brightness Compensation

在基于高分辨率遥感影像的道路提取中,阴影遮挡是导致提取的部分或整段道路缺失的重要因素,严重制约了道路提取的自动化过程,因此探索适用性强的阴影情况下道路提取方法对地图数据生产和地理大数据研究具有重要意义。本文针对传统的阴影系数修正方法难以消除植被、建筑上的阴影对道路提取带来的干扰,选用路面颜色不一、地物干扰少的郊区影像与地物丰富、路面地物阴影干扰严重的市区影像开展研究,提出了基于亮度补偿的阴影遮挡道路的提取方法。首先,在图像预处理的基础上,利用HSI阈值分割获取阴影区域;其次,在削弱蓝色分量信息后采用亮度补偿方法实现像素点空间域增强以及阴影区信息的恢复,在增大道路面阴影与周围环境差异的基础上,借助高效的分割算法实现阴影道路提取;最后,通过和由K-means聚类分割获取的非阴影道路进行合并,经细化处理最终实现阴影遮挡道路的完整提取。实验结果表明,此方法提取郊区与市区影像中阴影道路的正确率在80%以上,该方法能有效地提取阴影遮挡道路,消除其他阴影的干扰,降低阴影道路提取时的斑块破碎度,较好的保留道路的主体。

While extracting roads from high-resolution remote sensing imagery, shadow shielding is a main factor causing roads missing or defects, which could lead to difficulties for automatic road extraction. Therefore,developing methods for shaded road extraction with strong applicability has a great significance in map data production and research of geographical data. Traditional methods, such as the shadow coefficient amendment method, are difficult to remove the shadows of plants and buildings, and they would undermine the integrity of extracted roads. So, this paper proposed a feasible approach to extracting shaded roads based on brightness compensation and a high-performance segmentation method. First, after image preprocessing, a threshold segmentation method in HSI space was used to obtain the shadow area. Second, a combination of blue components suppression in the RGB space and divided linear strength was applied to enhance the pixel points in spatial domain and recover the information of the shaded areas, which made the difference between shaded roads and surrounding areas more obvious. Shaded roads were extracted by an efficient segmentation algorithm, and unshaded roads were calculated by K-means clustering segmentation. The initial value of clustering was based on color distribution in the HSI space. To ensure the integrity and details of extracted roads, the morphology method and contour repair algorithm were introduced into the extraction process after rough roads mergence.Results show that this method could extract shaded road successfully. For suburban roads, the integrity of extracted shaded roads was 96.84%. For urban roads, the accuracy was also higher than 80%. Compared with traditional methods based on the threshold segmentation in HSI, this method decreases the fragmentation of road patches while extraction, and keeps the integrity of the roads. This approach could be used for smart manufacturing and mapping of internet map data in high-resolution remote sensing imagery.