Mercury (Hg) is a heavy metal pollutant characterized by global migration, high bioaccumulation, and high biological toxicity. Soil Hg pollution seriously harms soil environmental quality and human health. The higher the content of Hg in the soil, the more serious the harm. Therefore, the acquisition of soil Hg content information is crucial for soil monitoring and restoration, and human health. The purpose of this study is to establish the best hyperspectral prediction model of soil Hg content in the study area. A total of 90 soil samples were collected from the mining areas and suburbs, and the Hg content was analyzed. Several preprocessing methods were applied to the hyperspectral data. Partial least-squares regression (PLSR), orthogonalized PLSR, random forest, and back propagation neural network (BPNN) were used to establish the relationship between soil hyperspectral data and soil Hg content to predict the soil Hg content. Specifically, the transferability of the best prediction model in yellow-brown and cinnamon soil data was discussed. The results showed that BPNN + standard normal variable + full-band (4 nm) is the optimum method to predict soil Hg content (Rtrain2=0.94, Rtest2=0.85). The transferability of the model is the best in yellow-brown soil. This research provides technical support for the rapid monitoring of soil Hg pollution information in central and eastern Shaanxi-Province, and the results enrich prediction models of soil Hg content in different soil types.
This paper proposes a 3D reconstruction method for the cross-scale narrow space containing thin structure. This method takes monocular image sequences as input and respectively constructs large-scale scene point cloud of main part based on incremental SfM and small-scale thin structure point cloud based on the curve method. The small-scale thin structure point cloud reconstructed individually alleviates the defect of incomplete point cloud from the large-scale scene. By introducing PCA for initial registration, alternatively iterating the closest point algorithm and the global optimization algorithm, we achieve a more precise registration for the thin structure point cloud. Then, a replacement algorithm is used to fuse it with the point cloud of the main part. The experiment containing two scenes verifies the effectiveness of our method, which reconstructs point cloud of the thin structure with high accuracy and retains the overall 3D information of the scene at the same time.
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