地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (2): 258-267.doi: 10.12082/dqxxkx.2020.190270

• 遥感科学与应用技术 • 上一篇    下一篇

基于亮度补偿的遥感影像阴影遮挡道路提取方法

何惠馨1, 范俊甫1,*(), 陈文贺1, 周玉科2, 张鹏1, 俞宵1   

  1. 1. 山东理工大学建筑工程学院,淄博 255000
    2. 中国科学院地理科学与资源研究所 生态系统网络观测与模拟院重点实验室,北京 100101
  • 收稿日期:2019-05-29 修回日期:2019-07-23 出版日期:2020-02-25 发布日期:2020-04-13
  • 通讯作者: 范俊甫 E-mail:fanjf@sdut.edu.cn
  • 作者简介:何惠馨(1993— ),女,浙江舟山人,硕士生,主要从事城市遥感。E-mail: hehx_sdut@163.com
  • 基金资助:
    国家重点研发计划项目(2017YFB0503500);国家自然科学基金项目(41601478);国家自然科学基金项目(41501425);山东省高等学校科技计划项目(J16LH03);山东理工大学青年教师发展支持计划项目(4072-115016)

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

HE Huixin1, FAN Junfu1,*(), CHEN Wenhe1, ZHOU Yuke2, ZHANG Peng1, YU Xiao1   

  1. 1. School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China
    2. Ecology Observing Network and Modeling Laboratory, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2019-05-29 Revised:2019-07-23 Online:2020-02-25 Published:2020-04-13
  • Contact: FAN Junfu E-mail:fanjf@sdut.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503500);National Natural Science Foundation of China(41601478);National Natural Science Foundation of China(41501425);Project of Shandong Province Higher Educational Science and Technology Program(J16LH03);Young Teacher Development Support Program of Shandong University of Technology(4072-115016)

摘要:

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。

关键词: 阴影遮挡, 道路提取, 亮度补偿, HSI, K-means聚类

Abstract:

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.

Key words: shadow shaded, road extraction, brightness compensation, HSI, K-means clustering