Hyperspectral water quality parameter inversion system and method based on one-dimensional convolutional neural network

Abstract

The invention discloses a hyperspectral water quality parameter inversion system based on a one-dimensional convolutional neural network. The system is used for measuring hyperspectral data of a waterbody at a selected point through a ground point spectrum actual measurement module; a water quality acquisition and analysis module is used for acquiring a water body sample at a selected point and analyzing to obtain water quality parameter concentration; an inversion model construction module is used for training parameters of a one-dimensional convolutional neural network by taking all the spectral band information as input and taking water quality parameter concentration as output so as to fit a complex nonlinear relationship between the spectral band information and the water quality parameter concentration; a hyperspectral data acquisition module is used for acquiring a hyperspectral remote sensing image of a monitored water area and obtaining the remote sensing reflectivity of eachpoint spectral band; and a water quality parameter inversion module is used for taking the spectral band information of each point as input to obtain the water quality parameter concentration of eachpoint in the monitored water area through inversion, and meanwhile, the system does not need to add a band screening sub-module and fully utilizes all band information.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral remote sensing water quality monitoring, and specifically relates to a hyperspectral water quality parameter inversion system and method based on a one-dimensional convolutional neural network. Background technique [0002] There are many rivers and lakes in my country, and with the continuous acceleration of the industrialization and urbanization process of the whole country, the water quality of my country's inland water bodies continues to deteriorate, and phenomena such as eutrophication and shrinkage of water body areas appear, so the abnormal conditions of inland water bodies are monitored and made The right response is of great strategic importance. Water quality parameters are optically active substances that affect the optical properties of water bodies in the natural environment, including chlorophyll a, suspended matter and yellow substances, which can measure the degree of eutroph...

AI Technical Summary

Problems solved by technology

[0008] In 2019, some scholars proposed a water quality inversion method based on convolutional neural network. This method is used to use a two-dimensional convolutional neural network to input a hyperspectral image block with a certain width and height, extract spatial and spectral features, and output water quality. However, this method requires the use of ground object spectrometers to measure the spectral information of each point in a water body with a certain width and height during field measurements, and it is necessary to collect water samples at each point in the water block, and obtain its water quality parameters through laboratory analysis. Concentration, the measurement process is complex and cumbersome and it is difficult to guarantee the accuracy of the measurement results

[0009] The above-mentioned traditional water quality parameter inversion method usually needs to calculate the correlation coefficient between the band or band combination and the water quality parameter concentration, and select the band or band combination with high correlation coefficient to build the inversion model, but part of the band information will be lost, resulting in a decrease in the inversion accuracy

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