A method for evaluating soil heavy metal concentration in hyperspectral imagery based on spatial weight constraints and variational self-encoding feature extraction

Abstract

The invention belongs to the field of soil environment monitoring and evaluation, and in particular relates to a hyperspectral image soil heavy metal concentration evaluation method based on spatial weight constraints and variational self-encoding feature extraction. The present invention first establishes a spatial weight constraint for each pixel in the hyperspectral image, and realizes the feature data extraction of soil heavy metal concentration modeling through a variational self-encoding method on the basis of the spatial weight constraint. A model is established between the features compressed by variational self-encoding, and finally the estimated value of soil heavy metal concentration at the unknown pixel is obtained. This method has the characteristics of non-contact, large-scale continuous geographical space, and strong generalization ability to evaluate the concentration of heavy metals in soil.

Description

technical field [0001] The invention relates to the field of soil environment monitoring and evaluation, in particular to a hyperspectral image soil heavy metal concentration evaluation method based on spatial weight constraints and variational self-encoding feature extraction. Background technique [0002] In recent years, the problem of environmental pollution in my country has continued to be serious. In order to monitor and evaluate the concentration of heavy metals in soil more efficiently, it is necessary to monitor the concentration of heavy metals in soil through new technologies and methods. The traditional soil heavy metal concentration monitoring method is field soil sampling for laboratory testing. This method is too time-consuming and labor-intensive, and the obtained soil heavy metals are point-like information, which cannot be obtained with high reliability even through geographic spatial interpolation methods. It cannot analyze and judge the continuous geogra...

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Problems solved by technology

[0008] In view of the existing hyperspectral remote sensing image detection technology, when estimating the concentration of heavy metals in soil, it relies too much on model learning, pays more attention to the numerical analysis of model building, ignores the characteristics of autocorrelation of ground object attributes in geography, and cannot overcome the prediction of large-scale research areas. The problem of spatial heterogeneity in time leads to the problem of low generalization ability of model migration

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