It is fundamental for 3D city maps to efficiently classify objects of point clouds in urban scenes. However, it is still a large challenge to obtain massive training samples for point clouds and to sustain the huge training burden. To overcome it, a knowledge-based approach is proposed. The knowledge-based approach can explore discriminating features of objects based on people's understanding of the surrounding environment, which exactly replaces the role of training samples. To implement the approach, a two-step segmentation procedure is carried out in this paper. In particular, Fourier Fitting is applied for second adaptive segmentation to separate points of multiple objects lying within a single group of the first segmentation. Then height difference and three geometrical eigen-features are extracted. In comparison to common classification methods, which need massive training samples, only basic knowledge of objects in urban scenes is needed to build an end-to-end match between objects and extracted features in the proposed approach. In addition, the proposed approach has high computational efficiency because of no heavy training process. Qualitative and quantificational experimental results show the proposed approach has promising performance for object classification in various urban scenes.

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Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective.

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The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments@en