KEYWORDS: Mining, Point clouds, Machine learning, Education and training, Detection and tracking algorithms, Data modeling, Random forests, Target recognition, Feature extraction, 3D metrology
In the process of acceptance measurement based on drone point cloud data, the mining cars in the open-pit mining area will generate a large number of pseudo terrain points in the 3D point cloud modeling process. If they are not extracted and removed, the accuracy of acceptance measurement will be seriously reduced. Therefore, how to accurately identify and extract mining car points from 3D point cloud data is the main technical issue for high-precision acceptance of mining sites based on unmanned aerial car oblique photogrammetry technology. Therefore, this article proposes a machine learning algorithm based method for extracting open-pit mining car point sets, which can effectively solve the problem of reduced acceptance accuracy caused by the existence of mining car point sets, thereby improving the accuracy of acceptance measurement. This provides important technical support for open-pit mining site acceptance measurement based on drone photogrammetry technology.
Existing super-resolution reconstruction algorithms for remote sensing images often struggle to fully extract and utilize features in complex scenes, and the reconstruction results are not optimal due to the influence of noise. We propose a reconstruction network model that combines deep dense residual module and sub-pixel convolution. This model connects multiple dense residual modules through recursive linking and introduces a channel attention mechanism to extract multiscale features from the images. During the feature reconstruction process, a sub-pixel convolution structure is introduced to reduce the impact of noise and enhance reconstruction performance. The model is tested on the UC Merced Land Use public dataset, and the results demonstrate that the reconstruction results of our proposed model algorithm have better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at different scales compared to the current mainstream reconstruction algorithm EDSR. It is proven that the proposed model can significantly improve the reconstruction quality through comparative analysis experiment, meeting the needs for high-resolution remote sensing image processing.
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