CN114357891A - Hyperspectral remote sensing quantitative inversion method for soil cadmium element content - Google Patents
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Abstract
The invention relates to application of a reflection spectrum in the field of ecological environment, in particular to a hyperspectral remote sensing quantitative inversion method of soil cadmium element content. The method comprises the steps of collecting soil samples in a research area, measuring the content of cadmium elements, obtaining reflection spectrums of the cadmium elements, carrying out spectrum transformation by adopting first-order differentiation, using the wavelength range of an image spectrum, selecting training and testing samples by random variation and a Kennard-Stone algorithm, optimally obtaining an optimal waveband combination by using a Grey wolf optimization algorithm, establishing a cadmium element content quantitative inversion model by combining a neural network technology, verifying based on a chemical testing result, and quantitatively inverting the content of the cadmium elements in the soil in the research area by using a CASI/SASI aviation hyperspectral remote sensing image. The method organically fuses the ground and the image spectrum, quickly and nondestructively obtains the content of the cadmium element in the soil in a large-scale research area, and has higher prediction precision.
Description
Technical Field
The invention relates to application of a reflection spectrum in the field of ecological environment, in particular to a hyperspectral remote sensing quantitative inversion method of soil cadmium element content.
Background
In 2019, cadmium and cadmium compounds were listed as toxic and harmful water pollutants. The cadmium element in the environment is mainly from crust and industrial pollution, and the plant food is easy to absorb three wastes discharged from metallurgy, smelting, ceramics, electroplating industry, chemical industry and the like due to the absorption of the cadmium element. Research shows that cadmium element is discharged into the environment through waste water and waste, cadmium-containing industrial waste diffuses and naturally settles, and is accumulated in soil around a factory, and beneficiation waste water of lead-zinc ore and related industrial waste water are discharged into ground water or permeate into underground water, and then pollute crops through irrigation, planting and other ways. With the proposal of the green industrial revolution concept, the problem that cadmium element diffuses into the environment to pollute the environment becomes one of the problems which need to be solved urgently. In addition, cadmium has been classified as a human carcinogen by the international cancer research institute and will also cause serious health damage to humans. Research has shown that over the past 30 years, the number of relevant studies on the source, level and distribution of cadmium elements in the environment has been increasing. Therefore, how to quantitatively analyze the content of cadmium in soil in a research area has important significance for further research of related technical methods, and meanwhile, the method can also provide technical support for researching the distribution of cadmium.
The traditional soil cadmium pollution monitoring method mainly adopts manual means with strong accuracy and high measurement precision, such as field fixed-point sampling, indoor chemical analysis and the like, but the method wastes time and labor and has certain destructiveness to the environment, and large-area and spatially continuous pollution distribution information is difficult to obtain by monitoring partial sampling points and profiles. The ground spectrometer can collect the spectral characteristics of the cadmium element in soil on site, but the operation area is relatively limited. The hyperspectral remote sensing has the characteristics of high spectral resolution, multiple and continuous wave bands and the like, can acquire fine ground object spectral information, and provides a new way for monitoring cadmium elements. In recent years, the trace element semi-quantitative inversion and regional content distribution inversion evaluation have shown good application value. With the emission of a series of hyperspectral remote sensing satellites at home and abroad, a series of quantitative inversion methods for the content of cadmium elements in soil emerge. However, the satellite image spectral information is easy to distort, the accuracy of information extraction is relatively low, and the information is difficult to be directly matched with the characteristic distribution of the actual soil cadmium element content. In addition, the image contains certain redundant information due to the high spectral resolution, and the dimension reduction of the sample is necessary, so that the inversion accuracy is further improved while the redundant features are removed.
In conclusion, the research on the relationship between the content of cadmium and the spectral reflectance at home and abroad is still in the beginning stage, and particularly, the research on the cadmium and the reflectance spectrum of soil in a large-scale research area is still under search. In view of the strategic importance of the soil cadmium element on environmental pollution prevention and control, the production value of the soil cadmium element and the like, the existing soil cadmium element geochemical analysis means and detection equipment have the limitations of precision and cost, and the distribution condition of the cadmium element in a large-scale research area is difficult to analyze. It is very necessary to develop a hyperspectral remote sensing quantitative inversion method for rapidly and nondestructively acquiring the content of cadmium elements in soil in a large-scale research area.
In view of this, the invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a hyperspectral remote sensing quantitative inversion method of soil cadmium element content, which organically fuses ground and image spectrums, is a hyperspectral remote sensing quantitative inversion method for quickly and nondestructively acquiring the soil cadmium element content in a large-range research area and has higher prediction precision.
A hyperspectral remote sensing quantitative inversion method for soil cadmium element content comprises the following steps:
(1) collecting a soil sample of a research area;
(2) testing the content of the cadmium element in the soil of the sample by adopting an inductively coupled plasma mass spectrometer;
(3) acquiring a sample reflection spectrum by using a surface feature spectrometer;
(4) smoothing the reflection spectrum, and performing spectrum transformation by using a first-order differential technology to control a baseline effect;
(5) selecting training and testing samples by random variation and Kennard-Stone algorithm by using the wavelength range of the image spectrum;
(6) selecting a wave band by adopting a wolf optimization algorithm to obtain an optimal wavelength combination, and constructing a soil cadmium element quantitative inversion model by combining a neural network;
(7) and (5) carrying out quantitative inversion on the content of cadmium elements in the soil in the research area by carrying out operation on the CASI/SASI aviation hyperspectral remote sensing image.
Preferably, the soil samples of the research area collected in step (1) are: 5 sub-sampling points are distributed in a quincunx manner within the range of 50m of each sampling point in each research area, stone, weed and tree root impurities in the sample are removed, then a mixed sample is synthesized by a quartering method, and the weight of each sample is more than 1 kg; and (3) air-drying, grinding and sieving the sample by a 200-mesh sieve, and dividing the sample into two sub-samples, wherein one sub-sample is used for the reflection spectrum measurement in the step (3), and the other sub-sample is used for the soil cadmium element content analysis in the step (2).
Preferably, the inductively coupled plasma mass spectrometer in the step (2) adopts NexIoN 350x type/CSY-066. And (2) accurately measuring the content of the soil cadmium element of the sample by a chemical method, and establishing a connection with the sample spectrum so as to establish a quantitative inversion model.
Preferably, the surface feature spectrometer in the step (3) is an SVC HR1024 surface feature spectrometer manufactured by Spectra Vista company of America.
Preferably, step (3) is: placing the sample in a sample container, and scraping the surface; the halogen lamp is used as a spectrum measurement light source, a white reference plate is used for radiometric calibration before measurement, a ground object spectrometer is used for obtaining a sample spectrum, the wavelength range is 350 nm-2500 nm, the distance between an optical fiber of the ground object spectrometer and a sample is 5-10 cm, and the angle of field of the optical fiber is 25 degrees.
Preferably, in step (4), a first order differential technique is used for correcting the baseline effect in the spectrum to eliminate non-chemical effects and establish a robust correction model; the first order differential equation is expressed as follows:
wherein,
finger-shaped
The first order differential of (a) is,
for the reflectivity of the next sampling band,
is the reflectivity of the last sampling band,
is the sampling interval.
Preferably, step (6) is:
step a: initializing parameters needed by the population and the algorithm, and setting the parameters:
including the number of wolf individuals
Maximum number of iterations
Convergence factor
Sum coefficient vector
、
(ii) a Wherein
Linearly decreasing from an initial value of 2 to 0 during the iteration,
and
is a random number between 0 and 1,
and
positively correlating, wherein each wolf individual in the population represents a wave band combination, and randomly initializing each individual position;
step b: and (3) predicting the content of cadmium in the test sample:
taking each individual grey wolf position as an input, taking each individual grey wolf position as a candidate wavelength combination, and continuously adjusting a weight and a threshold value minimization target function through back propagation to finally output a predicted value of the content of the cadmium element; continuously iterating through a neural network to obtain a cadmium element predicted value;
step c: calculating an individual fitness value:
obtaining the initial position of an individual in a wolf optimization algorithm, calculating the fitness of the individual, and determining the optimal value of the fitness
Sub-optimum value
And the next order of merit
Protection ofStoring corresponding individual positions;
step d: updating individual locations and parameters
、
、
The location update formula is as follows:
wherein,
、
、
respectively in step c
、
And
the corresponding individual position;
step e: decoding the individual positions, and weighting the absorption wavelength positions of the cadmium elements;
sorting all the individual fitness values in a descending order, and reserving an optimal value
Sub-optimum value
And the next order of merit
The corresponding individual position;
step f: recalculating individual fitness and updating
、
And
the corresponding individual position;
step g: judging a termination condition;
judging whether the current iteration number reaches the maximum iteration number
If it has, it will
And (4) constructing a soil cadmium element quantitative inversion model by combining the optimal waveband combination obtained by decoding and a neural network, and otherwise, repeating the steps c-f.
Preferably, in step b, the objective function is expressed as follows:
wherein,
in order to train the sample size,
as output variable dimension, which corresponds to the total number of bands,
and
respectively obtaining a true value and a predicted value of the cadmium element content of the sample;
in the step b, continuously iterating through a neural network to obtain a cadmium element predicted value, which is defined as follows:
wherein,
is the function of the activation of the function,
is the first
The output of each of the hidden layers is,
is connected with a hidden layer
And
the weight of (a) is determined,
is the first
The layer outputs the bias of the layer.
In the step c, the individual fitness calculating method comprises the following steps: and calculating by combining a neural network technology and utilizing an objective function to obtain the fitness value of the band combination corresponding to each group of codes, wherein a fitness calculation formula is as follows:
wherein,
representing an individual
The value of the fitness value of (a) is,
the prediction accuracy of the content of cadmium element corresponding to the individually selected wave band combination is shown,
which indicates the number of the selected bands,
weighting parameters to balance accuracy and number of bands.
Taking the individual position codes as candidate band combinations, calculating the fitness value of the band combination corresponding to each group of codes through an objective function, and keeping the optimal value of the wolf pack individuals
Sub-optimum value
And the next order of merit
When the algorithm satisfies the termination condition
And decoding and outputting the optimal band combination.
Preferably, the inversion model input item is the corresponding optimal band combination after decoding by the wolf optimization algorithm.
Compared with the prior art, the invention has the following beneficial effects:
(1) the regional inversion of the soil components is realized based on the aviation hyperspectral remote sensing image, and the possibility is provided for solving the distribution condition of the soil trace elements. The invention provides a hyperspectral remote sensing quantitative inversion method of soil cadmium element content, which is characterized by collecting soil samples of a plurality of research areas to test the cadmium element content, acquiring a reflection spectrum, smoothing the spectrum, representing the absorption characteristic of the cadmium element through first-order differential transformation, selecting training and testing samples by using the wavelength range of the image spectrum and combining random variation and a Kennard-Stone algorithm, selecting an optimal waveband combination through a wolf optimization algorithm, and simultaneously carrying out quantitative inversion modeling by combining a neural network, applying the method to CASI/SASI aviation hyperspectral images and analyzing the distribution condition of the soil cadmium element content of the research areas. This process organically fuses the ground with the image spectra so that both maintain similar trends. Therefore, on the basis of ensuring certain ground sampling, the invention trains a relatively accurate quantitative inversion model of the content of the cadmium element in the soil. The model can quickly and accurately carry out quantitative inversion on the content of the cadmium element in the soil in a large-range research area, and provides a valuable reference for further soil environment evaluation.
(2) The hyperspectral remote sensing sensor can monitor a large-range research area in real time, and the meta-heuristic algorithm has the characteristics of high convergence rate, strong optimization capability and the like, and can efficiently select a band combination with small correlation from all spectral characteristics. In addition, the soil cadmium element and the reflection spectrum generally present a nonlinear relation, and the neural network has good generalization capability when fitting the nonlinear relation, thereby bringing new ideas and precision improvement for quantitative inversion of the soil cadmium element. The invention organically fuses the ground and the image spectrum, is a hyperspectral remote sensing quantitative inversion method for quickly and nondestructively acquiring the content of the cadmium element in the soil in a large-range research area, realizes the prediction and evaluation of the content of the cadmium element in the soil in the research area, and searches the distribution condition of the cadmium element. The adopted spectrum transformation technology effectively eliminates the sample baseline effect and enhances the spectrum absorption characteristic, wherein the first order differential transformation has better enhanced spectrum characteristics; the adopted wolf optimization algorithm has the characteristics of simple structure, few parameters, easy realization and the like, and can quickly and efficiently select the optimal wave band combination in the spectrum sampling process; the adopted neural network has better prediction capability, stable performance and higher prediction precision. The method can rapidly and nondestructively carry out quantitative inversion on the content of the cadmium element in the soil, and meanwhile provides technical support for regional evaluation.
Drawings
In order to explain the specific technical solution of the present invention, the drawings required in the description of the related art will be briefly introduced.
FIG. 1 is a schematic overall flow diagram of the present invention;
FIG. 2 is a schematic flow chart of the gray wolf optimization algorithm of the present invention;
FIG. 3 is an aerial hyperspectral remote sensing image of a research area;
FIG. 4 is a diagram of the distribution of cadmium element content in soil in a research area obtained by quantitative inversion.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms also include the plural forms unless the context clearly dictates otherwise, and further, it is understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of the stated features, steps, operations, devices, components, and/or combinations thereof.
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
A hyperspectral remote sensing quantitative inversion method for soil cadmium element content is shown in figures 1-2 and comprises the following steps:
(1) sample collection
The sampling place is mainly a farmland in a research area, and a few sampling places are in a building land and a tailing pile, 264 soil samples of 0-20 cm plough layers are collected in total for chemical analysis and spectral measurement. The sampling time of the sample is 9-10 months in 2020, according to a sampling method specified in soil environment monitoring technical Specification (implemented by HJ/T166-. The sample testing and analysis is completed by a test center of a geophysical survey institute of the China metallurgical geological institute, after the sample is brought back to the test center, the sample is dried, ground and sieved (200 meshes), each obtained fine particle is divided into two sub-samples, one sub-sample is used for the reflection spectrum measurement in the step (3), and the other sub-sample is used for the cadmium element content analysis in the step (2).
(2) And (3) measuring the content of the cadmium element in the soil by adopting an inductively coupled plasma mass spectrometer NexIoN 350x type/CSY-066. The content of the soil cadmium element of the sample is accurately measured by a chemical method and is used for establishing connection with the sample spectrum so as to establish a quantitative inversion model.
(3) Reflectance spectrum acquisition
An SVC HR1024 terrain spectrometer manufactured by the American Spectra Vista company is used for measuring the spectrum of a soil sample with the wavelength range of 350-2500 nm, wherein the wavelength range covers a near infrared region and a short wave infrared region, and the total wavelength range is 1024 bands. Placing the sample in a sample container, and scraping the surface; a halogen lamp is used as a spectrum measurement light source, a white reference plate is used for radiation calibration before measurement, the distance between a spectrometer optical fiber and a sample is 5-10 cm, and the angle of field of the optical fiber is 25 degrees. The samples were averaged after 5 measurements to minimize measurement error. In order to eliminate the influence of the environment on spectral measurement, a part of ultraviolet spectral bands (with the wavelength less than 400 nm) with large noise are removed, and finally, spectral characteristics in the range of 400 nm-2500 nm are selected, and the total number of the spectral characteristics is 924 bands.
(4) Spectral transformation
The reflection spectrum preprocessing step and method play a crucial role in predicting the quality of results. However, since the content of cadmium element in the soil sample is too low to complete the spectral reflection, a series of spectral processing is required to enhance the spectral characteristics.
Firstly, the spectral curve in the reflection spectrum measurement stage usually has noise, and in order to effectively eliminate the spectral noise and simultaneously store the chemical information expressed by the spectrum, the spectrum smoothing processing is performed.
In addition, in order to eliminate the possibility of baseline effect brought by the particle size and enhance the spectral absorption characteristic of cadmium element in soil, first-order differential transformation is carried out. A first order differential technique is used to correct for baseline effects in the spectrum to eliminate non-chemical effects and to build a robust correction model. Specifically, the first derivative of the spectrum represents a measure of the slope of the spectral curve at each point, and the slope of the curve is not imaged by a purely additive baseline shift in the spectrum, and the first derivative minimizes background interference and baseline drift in the reflectance spectrum and effectively eliminates the baseline shift. The first order differential equation is expressed as follows:
wherein,
finger-shaped
The first order differential of (a) is,
for the reflectivity of the next sampling band,
is the reflectivity of the last sampling band,
is the sampling interval.
(5) Sample selection
For training a learner with good fitting effect, 264 samples subjected to spectral transformation are divided into training samples and verification samples before model construction, wherein the proportion of the training samples to the verification samples is 7: 3. the uniformity of the sample property distribution is significantly improved by using the wavelength range of the image spectrum, by using random variation and Kennard-Stone techniques, so that the training and testing samples cover the entire sample space. The technology is calculated based on Euclidean distance, the random variation factor is used for 10%, the positions of samples in a training set and a testing set are continuously updated in an iterative mode, and balance is kept between randomness and convergence. And selecting a sample with the largest decision coefficient, the smallest root mean square error and the large decision coefficient of the verification set for testing through iteration and cross verification.
(6) Band selection
Because the spectrum contains a large amount of redundant information, a large amount of time is consumed for carrying out quantitative inversion of the cadmium element in the soil in a large-scale research area. Therefore, band selection is necessary in the modeling process. The optimal band combination is obtained by adopting a wolf optimization algorithm, as shown in fig. 2, the specific process is as follows:
step a: initializing the population and the parameters needed by the algorithm.
Specifically, parameters required by a population and an algorithm are initialized, and various parameters are set: including the number of wolf individuals
Maximum number of iterations
Convergence factor
Sum coefficient vector
、
(ii) a Wherein
Linearly decreasing from an initial value of 2 to 0 during the iteration,
and
is a random number between 0 and 1,
and
positively correlating, wherein each wolf individual in the population represents a wave band combination, and randomly initializing each individual position;
step b: and predicting the content of cadmium element in the test sample.
The neural network is based on the gradient descent principle and mainly comprises two processes: information forward propagation and error reverse propagation. The technology takes the individual positions (namely candidate wavelength combinations) of the wolfsbane as input, and finally outputs the predicted value of the content of the cadmium element by continuously adjusting the weight and the threshold value minimization target function through back propagation. The objective function is expressed as follows:
wherein,
in order to train the sample size,
as output variable dimension, which corresponds to the total number of bands,
and
respectively obtaining a true value and a predicted value of the cadmium element content of the sample;
continuously iterating through a neural network to obtain a cadmium element predicted value, which is defined as follows:
wherein,
is the function of the activation of the function,
is the first
The output of each of the hidden layers is,
is connected with a hidden layer
And
the weight of (a) is determined,
is the first
The layer outputs the bias of the layer.
And the neural network updates the weight and the bias of each layer of neuron through iteration and finally outputs a predicted value of the content of the cadmium element.
Step c: an individual fitness value is calculated.
Obtaining the initial position of an individual in a wolf optimization algorithm, calculating the fitness of the individual, and determining the optimal value of the fitness
Sub-optimum value
And the next order of merit
And saving the corresponding individual position.
Specifically, the fitness value of the band combination corresponding to each group of codes is calculated by using an objective function in combination with a neural network technology, and higher prediction accuracy is obtained by selecting as little wavelength information as possible.
The fitness calculation formula is as follows:
wherein,
representing an individual
The value of the fitness value of (a) is,
the prediction accuracy of the content of cadmium element corresponding to the individually selected wave band combination is shown,
which indicates the number of the selected bands,
weighting parameters to balance accuracy and number of bands.
Step d: updating individual locations and parameters
、
、
Specifically, the gray wolf optimization algorithm considers
、
And
the potential position of the prey is closer, the position of the prey is judged by utilizing the information contained in the three parts, and other individuals gradually approach the prey according to the positions of the three parts. Updating individual locations and parameters
、
、
The specific updating formula is as follows:
wherein,
、
、
respectively in step c
、
And
the corresponding individual positions, namely the positions of the three wolfs.
Step e: decoding the individual positions and weighting the absorption wavelength positions of the cadmium elements.
Sorting all the individual fitness values in a descending order, and reserving an optimal value
Sub-optimum value
And the next order of merit
The corresponding individual position. The initial position vector of an individual in the wolf optimization algorithm is coded into a corresponding band combination by adopting a binary coding space, the binary coding space is designed to code a code '0' to indicate that a band corresponding to a component is not selected, and a code '1' to indicate that the band corresponding to the component is selected. Calculating through an objective function to obtain the fitness value of the corresponding band combination of each group of codes, and reserving the optimal value of the wolf pack individual
Sub-optimum value
And the next order of merit
When the algorithm satisfies the termination condition
Decoding is carried out, and the optimal band combination is output. According to the geochemical principle, the characteristics of the cadmium element in the soil at the absorption wavelength are extracted, the wave band combination is weighted, and the adaptability value of the wave band combination is evaluated. In principle, the spectral feature has an extreme value in the vicinity of the absorption peak, and no significant absorption feature appears for the absorption wavelength corresponding to the non-cadmium element.
Step f: recalculating individual fitness and updating
、
And
。
specifically, the content of cadmium element is predicted by combining the weight determined in step 4 and the wavelength combination determined in step 3 through a neural network. Calculating individual fitness value by using the objective function, recalculating fitness values of all wolfs, and updating the sorted individual fitness values
、
And
the corresponding individual position.
Step g: and (5) judging the termination condition.
Specifically, the algorithm determines whether the current iteration count reaches a maximum iteration count
If not, returning to the step c to continue execution, repeating the steps c-f, and searching the optimal wave band combination; if it has, it will
And (5) constructing a soil cadmium element quantitative inversion model by combining the optimal waveband combination obtained by decoding and a neural network.
(7) Quantitative inversion of soil cadmium element content
The image acquisition time of the research area is 10-14 points per day in 6-8 months in 2018, the acquired data are subjected to radiometric calibration, geometric correction and orthorectification through a system provided by ITRES company, FLAASH is adopted for atmospheric correction, and the SASI file is resampled to CASI, the spectral range is 400-2500 nm, and the total wave band number is 173. The method comprises the steps of performing first-order differential transformation on a reflection spectrum, selecting a training and testing sample by using a random variation and Kennard-Stone algorithm, obtaining an optimal wave band combination based on a Grey wolf optimization algorithm, directly applying the optimal wave band combination to an aviation hyperspectral remote sensing image, and quantitatively inverting the content of cadmium elements in soil in a research area through a neural network, wherein the result is shown in table 1, fig. 3 is the aviation hyperspectral remote sensing image in the research area, and fig. 4 is a distribution situation diagram of the content of cadmium elements in soil in the research area obtained through quantitative inversion.
TABLE 1 neural network training and testing results
For the training samples, the decision coefficient of the ground samples reached 0.9286, indicating that the predicted values have a high correlation with the measured values. For the test samples, the decision coefficient for the ground samples was further improved to 0.9948, indicating that the predicted values are very close to the measured value distribution. Experimental results show that the quantitative inversion method for the cadmium element in the soil has good adaptability to ground samples in a research area and can meet the requirements of practical application. And further testing is carried out through an aviation hyperspectral remote sensing image, and the cadmium element content in most regions of the research area is in the range of a risk screening value and a background value, so that earlier-stage related treatment measures are shown to effectively avoid further pollution of the whole soil environment. However, since small smelters are becoming the major economic source of the area and are difficult to eradicate completely in a short time, the area is still controlled by the risk management of part of the area cadmium element content, and subsequently more strict treatment measures are required to ensure that the rare earth element content of the area reaches the normal level. The invention provides a hyperspectral remote sensing quantitative inversion model of soil cadmium element content by taking a new male security area as a research area. Through sampling, measurement, data processing, modeling analysis and experimental verification, the reflection spectrum is proved to be applicable to rapid and nondestructive evaluation of the cadmium element pollution condition of the soil, and scientific and technical and theoretical bases are provided for pollution prevention and control work in research areas.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A hyperspectral remote sensing quantitative inversion method for soil cadmium element content is characterized by comprising the following steps:
(1) collecting a soil sample of a research area;
(2) testing the content of the cadmium element in the soil of the sample by adopting an inductively coupled plasma mass spectrometer;
(3) acquiring a sample reflection spectrum by using a surface feature spectrometer;
(4) smoothing the reflection spectrum, and performing spectrum transformation by using a first-order differential technology to control a baseline effect;
(5) selecting training and testing samples by random variation and Kennard-Stone algorithm by using the wavelength range of the image spectrum;
(6) selecting a wave band by adopting a wolf optimization algorithm to obtain an optimal wavelength combination, and constructing a soil cadmium element quantitative inversion model by combining a neural network;
(7) and (5) carrying out quantitative inversion on the content of cadmium elements in the soil in the research area by carrying out operation on the CASI/SASI aviation hyperspectral remote sensing image.
2. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 1, characterized in that the collection of soil samples in the research area in step (1) is: 5 sub-sampling points are distributed in a quincunx manner within the range of 50m of each sampling point in each research area, stone, weed and tree root impurities in the sample are removed, then a mixed sample is synthesized by a quartering method, and the weight of each sample is more than 1 kg; and (3) air-drying, grinding and sieving the sample by a 200-mesh sieve, and dividing the sample into two sub-samples, wherein one sub-sample is used for the reflection spectrum measurement in the step (3), and the other sub-sample is used for the soil cadmium element content analysis in the step (2).
3. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 1 is characterized in that the inductively coupled plasma mass spectrometer in the step (2) adopts NexIoN 350x type/CSY-066.
4. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 1 is characterized in that the surface feature spectrometer in the step (3) adopts an SVC HR1024 surface feature spectrometer.
5. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 4, characterized in that the step (3) is as follows: placing the sample in a sample container, and scraping the surface; the halogen lamp is used as a spectrum measurement light source, a white reference plate is used for radiometric calibration before measurement, a ground object spectrometer is used for obtaining a sample spectrum, the wavelength range is 350 nm-2500 nm, the distance between an optical fiber of the ground object spectrometer and a sample is 5-10 cm, and the angle of field of the optical fiber is 25 degrees.
6. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 1 is characterized in that in the step (4), a first order differential technology is used for correcting the baseline effect in the spectrum so as to eliminate non-chemical effects and establish a robust correction model; the first order differential equation is expressed as follows:
wherein,
finger-shaped
The first order differential of (a) is,
for the reflectivity of the next sampling band,
is the reflectivity of the last sampling band,
is the sampling interval.
7. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 1, characterized in that the step (6) is as follows:
step a: initializing parameters needed by the population and the algorithm, and setting the parameters:
including the number of wolf individuals
Maximum number of iterations
Convergence factor
Sum coefficient vector
、
(ii) a Wherein
Linearly decreasing from an initial value of 2 to 0 during the iteration,
and
is a random number between 0 and 1,
and
positively correlating, wherein each wolf individual in the population represents a wave band combination, and randomly initializing each individual position;
step b: and (3) predicting the content of cadmium in the test sample:
taking each individual grey wolf position as an input, taking each individual grey wolf position as a candidate wavelength combination, and continuously adjusting a weight and a threshold value minimization target function through back propagation to finally output a predicted value of the content of the cadmium element; continuously iterating through a neural network to obtain a cadmium element predicted value;
step c: calculating an individual fitness value:
obtaining the initial position of an individual in a wolf optimization algorithm, calculating the fitness of the individual, and determining the optimal value of the fitness
Sub-optimum value
And the next order of merit
Storing the corresponding individual positions;
step d: updating individual locations and parameters
、
、
The location update formula is as follows:
wherein,
、
、
respectively in step c
、
And
the corresponding individual position;
step e: decoding the individual positions, and weighting the absorption wavelength positions of the cadmium elements;
sorting all the individual fitness values in a descending order, and reserving an optimal value
Sub-optimum value
And the next order of merit
The corresponding individual position;
step f: recalculating individual fitness and updating
、
And
the corresponding individual position;
step g: judging a termination condition;
judging whether the current iteration number reaches the maximum iteration number
If it has, it will
And (4) constructing a soil cadmium element quantitative inversion model by combining the optimal waveband combination obtained by decoding and a neural network, and otherwise, repeating the steps c-f.
8. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 7 is characterized in that in the step b, the objective function is expressed as follows:
wherein,
in order to train the sample size,
as output variable dimension, which corresponds to the total number of bands,
and
respectively obtaining a true value and a predicted value of the cadmium element content of the sample;
in the step b, continuously iterating through a neural network to obtain a cadmium element predicted value, which is defined as follows:
wherein,
is the function of the activation of the function,
is the first
The output of each of the hidden layers is,
is connected with a hidden layer
And
the weight of (a) is determined,
is the first
The layer outputs the bias of the layer.
9. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 7 is characterized in that in the step c, the calculation method of individual fitness comprises the following steps: and calculating by combining a neural network technology and utilizing an objective function to obtain the fitness value of the band combination corresponding to each group of codes, wherein a fitness calculation formula is as follows:
wherein,
representing an individual
The value of the fitness value of (a) is,
the prediction accuracy of the content of cadmium element corresponding to the individually selected wave band combination is shown,
which indicates the number of the selected bands,
weighting parameters to balance accuracy and number of bands.
10. The hyperspectral remote sensing quantitative inversion method of soil cadmium element content according to claim 1, characterized in that the inversion model input item is the corresponding optimal band combination after decoding by the wolf optimization algorithm.
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