Research on DEM Gross Error Weight Decay Iterative Detection Algorithm
In order to study the way to eliminate the residual non-ground points in the filtered airborne LiDAR ground point cloud data and improve the accuracy of the produced digital elevation model(DEM),this paper proposed an iterative detection algorithm based on the attenuation of DEM gross error weights for the irregularly distributed data generated from the LiDAR point cloud data.The algorithm considered the residual non-ground points as DEM gross error.Based on the principle of local terrain similarity in geography,the algorithm determined the local window according to the density of data points and the degree of change of terrain and carried out quadratic surface fitting,solved the residual value of elevation of each data point in the local window,and constructed the related test to locate the gross error.At the same time,the algorithm drew on the idea of selective iterative gross error localization method,through iteration,constantly attenuating the weights of the gross error points,so as to continuously reduce the adverse effect on the local window quadratic surface fitting and the probability of ground points being misjudged as gross error.Through experimental verification,the algorithm can effectively improve the DEM gross error detection rate and greatly reduce the gross error misjudgment probability,and its gross error rejection rate reaches 98.02%,and the gross error misjudgment rate is 1.71%.
railway surveygross error detectionDEMLIDARiteration method with variable weights
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