In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.
As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes.
The performance of Multi-resolution Morphology Gradient(MMG)-based fault location scheme in power transmission lines will deteriorate when the noise is imposed on the transient signals. In this paper, a novel mathematical morphology filter Dierion is proposed to effectively reduce the noise in the transient signals and improve the performance of the accurate fault location using MMG. This proposed filter with parameters ( , ) extracts the th element in the sorted input signal according to the structure element, whose length is . Dilation and Erosion filter, the two basic operators in Mathematical Morphology (MM), are formulated in a unified mathematical framework defined by Dierion filter based on the assumption that the structure element is symmetric with respect to its origin. The general statistical characteristics of this proposed morphological filter are discussed in details. The results show that Dierion filter with parameters ( , ( + 1)/2), which namely is Median filter, performs better in noise reduction compared with Dilation and Erosion filters. The efficiency of Dierion filter's noise reduction is verified by a variety of simulations. Therefore, the MMG protection scheme is improved significantly to be noisy tolerant.
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