Abstract:Filtering is one of the core post-processing steps for airborne LiDAR point cloud. In recent years, the morphology-based filtering algorithms have proven to be a powerful and efficient tool for filtering airborne LiDAR point cloud. However, most traditional morphology-based algorithms have difficulties in preserving abrupt terrain features, especially when using larger filtering windows. In order to suppress the omission error caused by protruding terrain features, this paper proposes an improved morphological algorithm based on multi-level kriging interpolation. This algorithm is essentially a combination of progressive morphological filtering algorithm and multi-level interpolation filtering algorithm. The morphological opening operation is performed with filtering window gradually downsizing, while kriging interpolation is conducted at different levels according to the different filtering windows. This process is iterative in a top to down fashion until the filtering window is no longer greater than the preset minimum filtering window. Fifteen samples provided by the ISPRS commission were chosen to test the performance of the proposed algorithm. Experimental results show that the proposed method can achieve promising results not only in flat urban areas but also in rural areas. Comparing with other eight classical filtering methods, the proposed method obtained the lowest omission error, and preserved protruding terrain features better.
Abstract:Geocentric translation model (GTM) in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP) and new parameters determined using the arithmetic mean (AM) were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM) attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.
Airborne Light Detection and Ranging (LiDAR) is a popular active remote sensing technology that has been developing very rapidly in recent years. To solve the problems of low filtering accuracy of airborne LiDAR point clouds in complex terrain environments and avoiding too much human intervention, this paper proposes a point cloud filtering method based on active learning. In the proposed method, the initial training samples are acquired and marked automatically by multi-scale morphological operations. In so doing, no training samples are selected and labeled manually, i.e., the training samples are added gradually according to the oracle used in active learning. In this paper, the oracle is set to a sigmoid function of residuals from the points to the fitted surface. Subsequently, the training model is revised progressively using the updated training samples. Finally, the classification results are further optimized by a slope-based method. Three datasets with different filtering challenges provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to test the proposed method. Comparing with the other ten famous filtering methods, the proposed method can achieve the smallest average total error (5.51%). Thus, it can be concluded that the proposed method performs very well toward different terrain environments. INDEX TERMS Active learning, airborne LiDAR, point cloud filtering, oracle, training sample.
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