LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. The capsule network (CapsNet) can identify the spatial variations of features and is widely used in supervised learning. In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classification. The capsule network represents the features by vectors, which can account for the direction of the features and the relative position between the features. Therefore, more detailed feature information can be extracted. ResNet protects the integrity of information by passing input information to the output directly, which can solve the problem of network degradation caused by information loss in the traditional CNN propagation process to a certain extent. Two different LiDAR data sets and several classic machine learning algorithms are used for comparative experiments. The experimental results show that ResCapNet proposed in this article `improve the performance of LiDAR classification.

A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet

The transition through which the entirety of energy sector is passing at the moment causes the shift from the conventional generation capacities, based on fossil fuels, towards the renewable sources and sustainable energy production. This, in turn, can lead to the need for constructing the new transmission infrastructure, necessary for the evacuation of the produced energy and mitigation of the potential overloading of the existing elements. In order to evaluate the impact of the proposed new projects, the engineers need to have the proper information regarding the technical characteristics of those projects, with this, for the power lines, meaning as accurate the estimation of the line length as possible. This estimation is rather difficult to perform even for the experts, with some of them frequently turning towards the empirical methods instead. To avoid that, this paper proposes a technique for approximation of the length of the line connecting the two specified nodes of the grid, founded on the breadth-first search principle and the georeferenced projection of the system. This allows the inclusion of the zones through which the route cannot pass, such as the cities or the protected habitats, and also provides opportunity to expand the common two-dimensional definition of line’s length to the three-dimensional space, since the heights of the terrain sections can also be included. That, together with the ease of modifications for application on the various systems, could make this method a powerful tool for the system planning engineers, mitigating the risk of the potential errors that would be caused by the misestimations of the parameters of the new overhead lines.

Primena širinske pretrage i georeferenciranja za određivanje optimalne trase dalekovoda

Light detection and ranging is a relatively new technology, developed to complement traditional remote sensing technology. Digital aerial image provides reflectance information for land cover while LIDAR data provide more accurate geometrical information. There is much potential for fusing these two sources to achieve increased accuracy and utility for feature detection and classification. In order to assist Guangxi Electric Power Design Institute to improve their efficiency in power transmission line selection and design, field survey work, and reduce the cost, we have recently carried out the first power line project by using LIDAR and high resolution digital camera in China. The use of coloring LIDAR points by RGB of digital images makes it possible for obtaining high quality of in-room surveying, feature detection and classification. In addition, high quality and high resolution of DEM, DSM, DOM and visualization provides our customer not only with better selection and design of power line route, and reduced survey work, but also significant savings by 10 percent reduction of power line construction.

Lidar application in selection and design of power line route

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Lidar application in selection and design of power line route