基于GPS数据的露天矿道路网自动提取

传统栅格法提取路网精度较低,尤其在提取露天矿道路网时,路段缺失与偏移现象更加明显。针对这一问题,摒弃了传统栅格法通过增大栅格来保证道路网连通性的处理方式,以GPS数据存在偏移这一事实为根据,假设GPS数据偏移服从正态分布,提出了通过求取GPS轨迹点在道路上的概率进行栅格初始化的方法,建立了相应模型;采用二维中值滤波方法对初始栅格数据进行修正;对查表细化算法进行改进,采用改进的细化算法对道路网栅格图像进行细化处理,得到道路网骨架信息;采用追踪法实现道路矢量化。实验表明,该方法较传统栅格方法的覆盖率提高6.43%~11.54%,错误率降低42.13%~83.02%,为道路网提取工作提供了一种有效办法,揭示了栅格大小对于路网提取结果的重要影响。

Automatic extraction of road network in open-pit mine based on GPS data

The traditional grid method for extracting road network has a low accuracy,especially for the extraction of open-pit road network,the loss and offset of road is more significant.For solving this problem,the conventional solution is to enlarge the grid so that the connectivity can be ensured.However,in this paper,by assuming the GPS bias as a normal distribution,a method of rastering the GPS data by calculating the probability of track points on the road was proposed.On this basis,the median filter algorithm was deployed to preprocess the raster image.The index table thinning algorithm was improved,and this improved thinning algorithm was used to refine the grid image of the road network.Finally,the road network was translated into vectorization.Experimental results show that the coverage ratio of this method has been improved by 6.43% to 11.54% compared with the traditional grid method,and the error rate has been reduced by 42.13% to 83.02%.This paper provides an effective method for road network extraction and reveals the important influence of grid size on road network extraction results.