کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
507982 | 865162 | 2012 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A sequential iterative dual-filter for Lidar terrain modeling optimized for complex forested environments A sequential iterative dual-filter for Lidar terrain modeling optimized for complex forested environments](/preview/png/507982.png)
This paper introduces a sequential iterative dual-filter method for filtering Lidar point clouds acquired over rough and forested terrain and computing a digital terrain model (DTM). The method belongs to the family of virtual deforestation algorithms that iteratively detect and filter objects above-the ground surface. The method uses both points and raster models to do so. The algorithm performance was first tested over a complex badlands environment and compared to a reference model obtained using a traditional TIN-Iterative approach. It was further tested on a benchmark site of the ISPRS (site 5) representing mainly forests and slopes. Over badlands, the resulting DTM elevation RMSE was 0.14 m over flat areas, and increased to 0.28 m under forested and rough terrain. The later value was 12.5% lower than the one obtained with a TIN-Iterative approach. Over the ISPRS site, the TIN-Iterative model provided better results for 3 out of the 4 sample sites. But the proposed algorithm, still worked fairly well provided a total classification error of 5.52%, and is well ranked compared with other algorithms. While the TIN-iterative approach might work better with low density, the proposed one is a good alternative to process high density point cloud and compute DTMs suitable for modeling either hydrodynamic or morphological processes under forest cover at a local scale.
► We developed a Lidar filtering method to compute DTM under complex and forested areas.
► The method belong to the family of virtual deforestation algorithms.
► Algorithm performance was tested against other methods for a badlands and a ISPRS reference site.
► The proposed method performed better than the reference algorithm using high density Lidar data.
► under low point densities TIN-iterative method perform better.
Journal: Computers & Geosciences - Volume 44, July 2012, Pages 31–41