| 均值漂移与卡尔曼滤波相结合的遥感影像道路中心线追踪算法 |
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| 引用本文： | 曹帆之,朱述龙,朱宝山,李润生,孟伟灿. 均值漂移与卡尔曼滤波相结合的遥感影像道路中心线追踪算法[J]. 测绘学报, 2016, 45(2): 205-212. DOI: 10.11947/j.AGCS.2016.20140610 |
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| 作者姓名： | 曹帆之 朱述龙 朱宝山 李润生 孟伟灿 |
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| 作者单位： | 信息工程大学地理空间信息学院, 河南 郑州 450000 |
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| 基金项目： | 国家自然科学基金(41401462)The National Natural Science Foundation of China (41401462) |
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| 摘 要： | 基于模板匹配的道路追踪方法是道路提取中较实用的一类方法,但传统模板匹配方法主要以相关系数作为相似性测度,对车辆、树荫等遮挡敏感,不适用于高分辨率遥感影像道路提取。针对这一问题,本文采用一种稳健的相似性测度,设计了一种基于均值漂移的道路中心点匹配算法,克服了传统模板匹配对遮挡敏感的缺点;然后运用卡尔曼滤波,实现高分辨率遥感影像道路中心线追踪。试验表明,该方法能够准确提取高分辨率遥感影像道路中心线,对车辆、树荫等遮挡具有稳健性。
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| 关 键 词： | 高分辨率遥感影像 道路提取 道路中心线追踪 模板匹配 均值漂移 卡尔曼滤波 |
| 收稿时间： | 2014-12-05 |
| 修稿时间： | 2015-08-20 |
Tracking Road Centerl ines from Remotely Sensed Imagery Using Mean Shift and Kalman Filtering |
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| CAO Fanzhi,ZHU Shulong,ZHU Baoshan,LI Runsheng,MENG Weican. Tracking Road Centerl ines from Remotely Sensed Imagery Using Mean Shift and Kalman Filtering[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(2): 205-212. DOI: 10.11947/j.AGCS.2016.20140610 |
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| Authors: | CAO Fanzhi ZHU Shulong ZHU Baoshan LI Runsheng MENG Weican |
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| Affiliation: | Institute of Geosgatial Information, Information Engineering University, Zhengzhou 450000, China |
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| Abstract: | Road tracking based on template matching is one class of practical methods of road extraction. However,the conventional methods of template matching mainly utilize correlation coefficient as the similarity measure.As a result,these algorithms are sensitive to occlusions caused by vehicles and trees and are unsuitable for road extraction from high-resolution remotely sensed imagery.To address this problem,this paper designs a road center matching algorithm based on mean shift utilizing a robust similarity measure,which overcomes the sensitivity of correlation coefficient matching to occlusions;then Kalman filter is utilized to track road centerlines from high-resolution remotely sensed imagery. Experimental results demonstrate that the proposed method can extract road centerlines from high-resolution remotely sensed imagery accurately and is robust to occlusions caused by vehicles and trees. |
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| Keywords: | high-resolution remotely sensed imagery road extraction road centerline tracking template matching mean shift Kalman filter |
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