CN107478580B - Soil heavy metal content estimation method and device based on hyperspectral remote sensing - Google Patents
Soil heavy metal content estimation method and device based on hyperspectral remote sensing Download PDFInfo
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Abstract
The invention provides a soil heavy metal content estimation method and device based on hyperspectral remote sensing, wherein the method comprises the following steps: acquiring a hyperspectral remote sensing reflectivity spectrum of soil to be detected; acquiring the reflectivity of a preset waveband corresponding to the type of the heavy metal to be detected according to the reflectivity spectrum; and calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity. According to the invention, the content of the heavy metal to be detected in the soil is calculated by utilizing the reflectivity of the preset wave band (the sensitive wave band of the characteristic spectrum of the active substance for adsorbing and fixing the heavy metal to be detected in the soil) in the reflectivity spectrum, so that the heavy metal content in the soil with high precision can be obtained, and the wave band redundancy is reduced. The invention realizes the estimation of the heavy metal content of the soil with higher precision by using the reflectivity of the sensitive wave band of a small amount of characteristic spectrum with definite physical significance, and has good applicability.
Description
Technical Field
The invention relates to the technical field of measurement, in particular to a soil heavy metal content estimation method and device based on hyperspectral remote sensing.
Background
Heavy metal contamination of soil has become a serious environmental problem. Especially for the areas with weak economic foundation and backward technology level. Heavy metal pollution of soil is increasingly serious due to unreasonable development of natural resources, discharge of production and domestic sewage, accumulation of waste residues and the like. Heavy metal contaminants, after entering the environment, are diffused as water and air circulate, causing greater contamination from point sources to area sources. Since most heavy metals are not easily decomposed, the heavy metals are accumulated in the environment through a biological chain, which poses a serious threat to human health.
The traditional soil heavy metal pollution evaluation method is realized by collecting soil samples in the field and determining the content of heavy metals through chemical analysis in a laboratory, and then performing space difference. The evaluation method based on the preferential sampling points is difficult to meet the requirements of planar, large-scale and rapid measurement of the heavy metal in the soil. And for large-scale heavy metal pollution investigation, the traditional heavy metal pollution investigation method needs a large amount of soil samples and laboratory chemical analysis, is time-consuming and labor-consuming, has huge cost and is not suitable for large-scale pollution investigation.
Remote sensing has the characteristics of large observation range, short period, less limitation by the surface condition and the like. Hyperspectral remote sensing is a new remote sensing mode developed in the 80 s of the 20 th century. The hyperspectral remote sensing can obtain a continuous spectral curve of a target object. By analyzing the interaction between the ground objects and the electromagnetic waves and between different ground objects, the information extraction can be realized. The soil reflectance spectrum is a comprehensive reflection of soil properties. Factors that affect the reflectance spectrum of soil include organic matter, iron oxides, clay minerals, parent soil, water content, and soil particle size, among others. Organic matter, iron oxide and clay minerals are main soil spectrum active substances. The intrinsic relationship between heavy metals and soil spectral active substances is a mechanism for estimating the content of heavy metals by using soil reflectivity spectra.
The soil heavy metal content estimation study based on reflectance spectra began in 1990 s. In recent years, with the development of spectrometers and multivariate regression algorithms, the content of heavy metals in soil based on reflectance spectra has undergone rapid development. The land type with the estimated heavy metal content covers mining areas, cultivated land and river sediments; the heavy metal elements include Cd, Hg, Cu, Pb, Cr, Zn, Ni and As. Common soil heavy metal content estimation modeling algorithms include Partial Least Squares Regression (PLSR), Multiple Linear Regression (MLR), Univariate Regression (UR), Artificial Neural Networks (ans), and the like. The partial least squares is the most widely used algorithm in soil heavy metal content estimation modeling. In addition, high correlation exists between adjacent wave bands of the hyperspectral data, so that the data volume is large and the redundancy phenomenon is prominent.
At present, soil heavy metal content estimation research based on hyperspectral remote sensing reflectance spectrums does not select the acquired soil reflectance spectrums, and all reflectance spectrum bands are adopted for soil heavy metal content estimation, so that the calculation precision is low, and the application of hyperspectral remote sensing in soil heavy metal content estimation and mapping is limited.
Disclosure of Invention
The invention provides a soil heavy metal content estimation method and device based on hyperspectral remote sensing, which at least partially solves the technical problem.
In a first aspect, the invention provides a soil heavy metal content estimation method based on hyperspectral remote sensing, which comprises the following steps:
acquiring a hyperspectral remote sensing reflectivity spectrum of soil to be detected;
acquiring the reflectivity of a preset waveband corresponding to the type of the heavy metal to be detected according to the reflectivity spectrum; the preset wave band is obtained according to a characteristic spectrum of an active substance used for adsorbing and fixing the heavy metal to be detected in soil;
and calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity.
Preferably, after obtaining the reflectivity spectrum of the soil to be detected, before obtaining the reflectivity of the preset waveband corresponding to the heavy metal category to be detected according to the reflectivity spectrum, the method further includes:
removing noise from the reflectance spectrum.
Preferably, the removing noise of the reflectance spectrum includes:
removing the spectrum of a wave band with the signal-to-noise ratio lower than a preset value in the reflectivity spectrum;
and filtering the residual spectrum in the reflectivity spectrum of the spectrum from which the wave band with the signal-to-noise ratio lower than the preset value is removed by adopting a segmented filtering algorithm.
Preferably, after removing the noise of the reflectivity spectrum, before obtaining the reflectivity of the preset waveband corresponding to the category of the heavy metal to be detected according to the reflectivity spectrum, the method further includes:
and acquiring the reflectivity spectrum after the envelope curve is removed by adopting an envelope curve removing method for the reflectivity spectrum after the noise is removed.
Preferably, before the obtaining of the reflectivity spectrum of the soil to be measured, the method further comprises:
acquiring a reflectivity spectrum of a soil sample and the content of preset heavy metal of the soil sample;
acquiring a characteristic spectrum of an active substance used for adsorbing and fixing the preset heavy metal in the soil sample according to the reflectivity spectrum of the soil sample;
acquiring the reflectivity of the wave bands of the characteristic spectrum, calculating the content of heavy metal by adopting a genetic algorithm of preset times according to the reflectivity, and selecting a group of wave bands with the minimum difference value with the content of the preset heavy metal;
and constructing the soil heavy metal content estimation model according to the reflectivity of the selected wave band and the content of the preset heavy metal.
In a second aspect, the present invention further provides a soil heavy metal content estimation device based on hyperspectral remote sensing, including:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a reflectance spectrum of the hyperspectral remote sensing of the soil to be detected;
the second acquisition unit is used for acquiring the reflectivity of a preset waveband corresponding to the type of the heavy metal to be detected according to the reflectivity spectrum; the preset wave band is obtained according to a characteristic spectrum of an active substance used for adsorbing and fixing the heavy metal to be detected in soil;
and the calculating unit is used for calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity.
Preferably, the method further comprises the following steps:
and the noise removing unit is used for removing the noise of the reflectivity spectrum before acquiring the reflectivity of the preset waveband corresponding to the heavy metal category to be detected according to the reflectivity spectrum after acquiring the reflectivity spectrum of the soil to be detected.
Preferably, the method further comprises the following steps:
the third acquisition unit is used for acquiring the reflectivity spectrum of the soil sample and the content of the preset heavy metal of the soil sample before acquiring the reflectivity spectrum of the soil to be detected;
the fourth acquisition unit is used for acquiring a characteristic spectrum of an active substance used for adsorbing and fixing the preset heavy metal in the soil sample according to the reflectivity spectrum of the soil sample;
a fifth acquiring unit configured to acquire a reflectance of a wavelength band of the characteristic spectrum;
a selection unit for calculating the content of heavy metal by using a preset number of genetic algorithms according to the reflectivity, and selecting a group of wave bands with the smallest difference value with the content of the preset heavy metal;
and the construction unit is used for constructing the soil heavy metal content estimation model according to the reflectivity of the selected wave band and the content of the preset heavy metal.
In a third aspect, the present invention also provides an electronic device, including: a processor, a memory, a bus, and a computer program stored on the memory and executable on the processor;
the processor and the memory complete mutual communication through the bus;
the processor implements the method when executing the computer program.
In a fourth aspect, the invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method.
According to the technical scheme, the content of the heavy metal to be detected in the soil is calculated by utilizing the reflectivity of the preset waveband (the sensitive waveband of the characteristic spectrum of the active substance for adsorbing and fixing the heavy metal to be detected in the soil) in the reflectivity spectrum, so that the high-precision content of the heavy metal in the soil can be obtained, and the waveband redundancy is reduced. The method realizes the estimation of the heavy metal content of the soil with higher precision by using the reflectivity of the sensitive waveband of a small number of characteristic spectra with definite physical significance, has good applicability due to the definite physical significance, and is favorable for promoting the application of aviation and satellite hyperspectral remote sensing in the field of monitoring of heavy metal pollution in the soil.
Drawings
FIG. 1 is a flow chart of a soil heavy metal content estimation method based on hyperspectral remote sensing according to an embodiment of the invention;
FIG. 2a is a reflectance spectrum of a soil sample measured in a laboratory;
FIG. 2b is a spectrum after filtering the reflectivity spectrum segment SG shown in FIG. 2 a;
FIG. 3a is a graph showing the results of the estimation of the Ni content of heavy metals in all visible-short wave infrared bands;
FIG. 3b is a diagram showing the result of estimating the content of heavy metal Ni in soil by using characteristic bands of organic matters and clay minerals;
FIG. 4 is a schematic block diagram of a soil heavy metal content estimation device based on hyperspectral remote sensing according to an embodiment of the invention;
fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a soil heavy metal content estimation method based on hyperspectral remote sensing according to an embodiment of the invention.
As shown in FIG. 1, the soil heavy metal content estimation method based on hyperspectral remote sensing comprises the following steps:
s101, acquiring a hyperspectral remote sensing reflectivity spectrum of soil to be detected;
s102, acquiring the reflectivity of a preset wave band corresponding to the type of the heavy metal to be detected according to the reflectivity spectrum; the preset wave band is obtained according to a characteristic spectrum of an active substance used for adsorbing and fixing the heavy metal to be detected in soil;
it is worth mentioning that the preset wave band may be a sensitive wave band in a characteristic spectrum according to an active substance in soil for adsorbing and immobilizing the heavy metal to be detected.
S103, calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity.
According to the embodiment of the invention, the content of the heavy metal to be detected in the soil is calculated by utilizing the reflectivity of the preset waveband (the sensitive waveband of the characteristic spectrum of the active substance for adsorbing and fixing the heavy metal to be detected in the soil) in the reflectivity spectrum, so that the high-precision content of the heavy metal in the soil can be obtained, and the waveband redundancy is reduced. The method realizes the estimation of the heavy metal content of the soil with higher precision by using the reflectivity of a sensitive waveband of a small number of characteristic spectra with definite physical significance, has good applicability due to the definite physical significance, and is favorable for promoting the application of aviation and satellite hyperspectral remote sensing in the field of monitoring of heavy metal pollution in the soil.
As a preferred embodiment, after S101 and before S102, the method further includes:
removing noise from the reflectance spectrum.
In a specific embodiment, removing noise from the reflectance spectrum comprises:
removing the spectrum of a wave band with the signal-to-noise ratio lower than a preset value in the reflectivity spectrum;
and filtering the residual spectrum in the reflectivity spectrum of the spectrum from which the wave band with the signal-to-noise ratio lower than the preset value is removed by adopting a segmented filtering algorithm.
As a preferred embodiment, after removing the noise of the reflectivity spectrum and before S102, the method further includes:
and acquiring the reflectivity spectrum after the envelope curve is removed by adopting an envelope curve removing method for the reflectivity spectrum after the noise is removed.
In one embodiment, spectral noise is removed by respectively adopting a wave band interception and segmentation Savitzky-Golay (SG) filtering algorithm according to the degree of influence of noise on the soil reflectivity spectrum. For the soil reflection spectrogram with the measurement waveband of 350-. According to the degree of noise of the 400-2400nm interval spectrum, SG filtering is carried out by adopting 7 points and a 2-degree polynomial for a visible light region with less noise influence, and SG filtering is carried out by adopting a 14-point 2-degree polynomial for a region except the visible light region.
And (3) enhancing the spectral characteristics of the soil reflectivity spectrum after the noise is removed by adopting an envelope removal (CR) algorithm. The spectral curve absorption depth D is defined as:
D＝1 Rb/Rc
wherein R isbIs the spectral curve reflectivity, RcIs the envelope at that band.
As a specific embodiment, before S101, the method further includes:
acquiring a reflectivity spectrum of a soil sample and the content of preset heavy metal of the soil sample;
acquiring a characteristic spectrum of an active substance used for adsorbing and fixing the preset heavy metal in the soil sample according to the reflectivity spectrum of the soil sample;
acquiring the reflectivity of the wave bands of the characteristic spectrum, calculating the content of heavy metal by adopting a genetic algorithm of preset times according to the reflectivity, and selecting a group of wave bands with the minimum difference value with the content of the preset heavy metal;
it is worth noting that the wavelength band selected in this step is the sensitive wavelength band in the characteristic spectrum.
And constructing the soil heavy metal content estimation model according to the reflectivity of the selected wave band and the content of the preset heavy metal.
The method for constructing the soil heavy metal content estimation model is described in a specific embodiment.
S1: acquiring a soil sample reflectivity spectrum and a soil sample heavy metal content;
the method mainly comprises the following steps:
measuring the reflectivity spectrum of soil samples in a laboratory, measuring the reflectivity spectrum of 74 soil samples at the wave band of 350-2500nm by using PSR-3500(SpectraLevolution Inc., Lawrence, MA, USA), and the measurement result is shown in FIG. 2 a;
according to the regulations of soil environmental quality standard (GB 15618) 19951996-03-01) on the determination of the heavy metal content in soil, a soil sample is digested by hydrochloric acid-nitric acid-perchloric acid, and then the Ni content in the soil is determined by a flame atomic absorption spectrophotometry.
S2: removing spectral noise and enhancing characteristics of soil;
the method mainly comprises the following steps:
according to the degree of influence of noise on the soil reflectivity spectrum, spectral noise is removed by adopting a wave band interception and segmented Savitzky-Golay (SG) filtering algorithm. Because the signal-to-noise ratio of the spectrometer at the two ends of the spectrum measurement range is low, the data is seriously influenced by noise, and therefore, the soil reflectivity spectrum in the interval of 350-400nm and 2400-2500nm is removed. According to the degree of noise of the 400-plus 2400nm interval spectrum, SG filtering is carried out on the visible light region with less noise influence by adopting 7 points and a 2-degree polynomial, SG filtering is carried out on the region except the visible light region by adopting the 2-degree polynomial with 14 points, and the sectional SG filtering result is shown in figure 2 b;
and (3) enhancing the spectral characteristics of the soil reflectivity spectrum after the noise is removed by adopting an envelope elimination (CR) algorithm. The spectral curve absorption depth D is defined as:
D＝1 Rb/Rc
wherein R isbIs the spectral curve reflectivity, RcIs the envelope at that band.
S3: determining main adsorption and fixation substances in the soil according to the heavy metal category of the soil;
for heavy metal Ni, the adsorption effect of soil organic matters on Ni is strongest, and the fixing effect of clay minerals on soil Ni is strongest.
S4: extracting a characteristic spectrum of the heavy metal element from the soil reflectivity spectrum;
the method mainly comprises the following steps:
the influence of organic substances on the soil reflectivity spectrum is mainly 400-1100nm, the maximum influence is near 410nm and 600-800 nm; the effect of clay minerals on the reflectance spectrum of soil is mainly concentrated around 1400nm, 1900nm and 2200 nm. Therefore, the absorption peaks at 800nm, 410nm, 1400nm, 1900nm and 2200nm were extracted as the characteristic spectra of the heavy metal Ni;
s5: selecting the wave bands of the extracted soil reflectivity spectrum;
the soil reflectance spectrum extracted at S4 was subjected to band selection using a Genetic Algorithm (GA). The parameter setting of the GA algorithm comprises the following steps: the population number is as follows: 20; maximum algebra: 120 of a solvent; difference generation: 10 percent; variation frequency: 10 percent;
and (5) running the genetic algorithm, and selecting a group of wave bands used in the experiment with the highest heavy metal content estimation precision.
S6: constructing a soil heavy metal content estimation model according to the reflectivity corresponding to the selected wave band and combining the heavy metal content to estimate the heavy metal content of the soil
And (3) according to the soil reflectivity spectrum obtained by wave band selection, combining the Ni content data of the soil sample, and establishing a soil heavy metal content estimation model by adopting Partial Least Square Regression (PLSR) to estimate the soil heavy metal content.
The estimation accuracy of the Ni content of the soil obtained by the method for estimating the heavy metal content of the soil is shown in the table 1.
TABLE 1
Wherein all bands represent all visible-short wave infrared spectrum bands after noise bands are removed;
PCs (principal components) composition scores representing the number of variables of partial least squares regression;
RMSEP (root mean square error of prediction) represents the root mean square error of the soil heavy metal content estimation model in the soil Ni content estimation;
RPD (ratio of performance to estimation) relative analysis error is equal to the ratio of the labeling difference of the verification sample set and the prediction root mean square error;
R2(coeffient of determination) determines the coefficients.
FIG. 3a is the estimation of the Ni content of the heavy metal in the soil using the entire visible-short wave infrared band, and FIG. 3b is the estimation of the Ni content of the heavy metal in the soil obtained in this example. The estimation result of fig. 3b is better than that of fig. 3a, regardless of the estimation accuracy from the whole or the single-point soil Ni content. Therefore, the method provided by the invention can be proved to realize higher-precision soil heavy metal content estimation by utilizing the reflectivity of the sensitive wave band with definite physical significance and a small quantity of characteristic spectra. The method has good applicability, can be applied to laboratory spectrum modeling, field spectrum modeling and image spectrum modeling, is expected to directly apply a laboratory and field heavy metal content estimation model to a hyperspectral remote sensing image, realizes large-scale soil heavy metal hyperspectral remote sensing quantitative mapping, and is beneficial to promoting the application of aviation and satellite hyperspectral remote sensing in the field of soil heavy metal pollution monitoring.
Fig. 4 is a schematic block diagram of a soil heavy metal content estimation device based on hyperspectral remote sensing according to an embodiment of the invention.
The invention shown in fig. 4 also provides a soil heavy metal content estimation device based on hyperspectral remote sensing, which comprises:
a first obtaining unit 401, configured to obtain a reflectance spectrum of hyperspectral remote sensing of soil to be measured;
a second obtaining unit 402, configured to obtain, according to the reflectivity spectrum, a reflectivity of a preset waveband corresponding to the category of the heavy metal to be detected; the preset wave band is obtained according to a characteristic spectrum of an active substance used for adsorbing and fixing the heavy metal to be detected in soil;
and a calculating unit 403, configured to calculate, according to the reflectivity, the content of the heavy metal to be detected in the soil by using a pre-established soil heavy metal content estimation model.
As a preferred embodiment, further comprising:
and the noise removing unit is used for removing the noise of the reflectivity spectrum before acquiring the reflectivity of the preset waveband corresponding to the heavy metal category to be detected according to the reflectivity spectrum after acquiring the reflectivity spectrum of the soil to be detected.
As a preferred embodiment, further comprising:
the third acquisition unit is used for acquiring the reflectivity spectrum of the soil sample and the content of the preset heavy metal of the soil sample before acquiring the reflectivity spectrum of the soil to be detected;
the fourth acquisition unit is used for acquiring a characteristic spectrum of an active substance used for adsorbing and fixing the preset heavy metal in the soil sample according to the reflectivity spectrum of the soil sample;
a fifth acquiring unit configured to acquire a reflectance of a wavelength band of the characteristic spectrum;
a selection unit for calculating the content of heavy metal by using a preset number of genetic algorithms according to the reflectivity, and selecting a group of wave bands with the smallest difference value with the content of the preset heavy metal;
and the construction unit is used for constructing the soil heavy metal content estimation model according to the reflectivity of the selected wave band and the content of the preset heavy metal.
According to the soil heavy metal content estimation device based on the hyperspectral remote sensing and the soil heavy metal content estimation method based on the hyperspectral remote sensing, which are disclosed by the invention, are in one-to-one correspondence, so that the soil heavy metal content estimation device based on the hyperspectral remote sensing is not described in detail.
Fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention. An electronic device as shown in fig. 5, comprising: a processor 501, a memory 502, a bus 503, and computer programs stored on the memory 502 and executable on the processor 501;
the processor 501 and the memory 502 complete communication with each other through the bus 503;
when the processor 501 executes the computer program, the method provided by the foregoing method embodiments is implemented, for example, including: acquiring a reflectivity spectrum of soil to be detected; acquiring the reflectivity of a preset waveband corresponding to the type of the heavy metal to be detected according to the reflectivity spectrum; and calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the computer program implements the method provided in the foregoing method embodiments, for example, the method includes: acquiring a reflectivity spectrum of soil to be detected; acquiring the reflectivity of a preset waveband corresponding to the type of the heavy metal to be detected according to the reflectivity spectrum; and calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means/systems for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. The terms "upper", "lower", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and/or permutation of these aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
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; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (7)
1. A soil heavy metal content estimation method based on hyperspectral remote sensing is characterized by comprising the following steps:
acquiring a hyperspectral remote sensing reflectivity spectrum of soil to be detected;
removing noise of the reflectivity spectrum, specifically: according to the degree of influence of noise on the soil reflectivity spectrum, spectral noise is removed by respectively adopting a wave band interception and segmentation Savitzky-Golay filtering algorithm, according to the degree of noise on the spectrum in the 400-plus 2400nm interval, SG filtering is carried out on a visible light region by adopting 7 points and a 2-degree polynomial, and SG filtering is carried out on a region except the visible light region by adopting a 14-point 2-degree polynomial;
adopting an envelope removal method to obtain the reflectivity spectrum with the envelope removed from the reflectivity spectrum with the noise removed;
acquiring the reflectivity of a preset waveband corresponding to the category of the heavy metal to be detected according to the reflectivity spectrum; the preset wave band is obtained according to a characteristic spectrum of an active substance used for adsorbing and fixing the heavy metal to be detected in soil;
the preset wave band is determined by the following steps: extracting a characteristic spectrum of the active substance of the heavy metal to be detected, and selecting a waveband by adopting a genetic algorithm to obtain the preset waveband;
if the main adsorption and fixation substance of the heavy metal to be detected is an organic matter, the value ranges of the characteristic spectrum are a 410nm absorption peak and a 600-800nm absorption peak, and if the main adsorption and fixation substance of the heavy metal to be detected is a clay mineral, the value ranges of the characteristic spectrum are a 1400nm absorption peak, a 1900nm absorption peak and a 2200nm absorption peak;
and calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity, wherein the soil heavy metal content estimation model is established by adopting a partial least square algorithm.
2. The method of claim 1, wherein removing noise from the reflectance spectrum comprises:
removing the spectrum of a wave band with the signal-to-noise ratio lower than a preset value in the reflectivity spectrum;
and filtering the residual spectrum in the reflectivity spectrum of the spectrum from which the wave band with the signal-to-noise ratio lower than the preset value is removed by adopting a segmented filtering algorithm.
3. The method of claim 1, wherein prior to obtaining the reflectance spectrum of the soil under test, the method further comprises:
acquiring a reflectivity spectrum of a soil sample and the content of preset heavy metal of the soil sample;
acquiring a characteristic spectrum of an active substance used for adsorbing and fixing the preset heavy metal in the soil sample according to the reflectivity spectrum of the soil sample;
acquiring the reflectivity of the wave bands of the characteristic spectrum, calculating the content of heavy metal by adopting a genetic algorithm of preset times according to the reflectivity, and selecting a group of wave bands with the minimum difference value with the content of the preset heavy metal;
and constructing the soil heavy metal content estimation model according to the reflectivity of the selected wave band and the content of the preset heavy metal.
4. The utility model provides a soil heavy metal content estimation device based on hyperspectral remote sensing which characterized in that includes:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a reflectance spectrum of the hyperspectral remote sensing of the soil to be detected;
the noise removing unit is used for removing the noise of the reflectivity spectrum after acquiring the reflectivity spectrum of the soil to be detected and before acquiring the reflectivity of the preset waveband corresponding to the heavy metal category to be detected according to the reflectivity spectrum, and specifically comprises the following steps: according to the degree of influence of noise on the soil reflectivity spectrum, spectral noise is removed by respectively adopting a wave band interception and segmentation Savitzky-Golay filtering algorithm, according to the degree of noise on the spectrum in the 400-plus 2400nm interval, SG filtering is carried out on a visible light region by adopting 7 points and a 2-degree polynomial, and SG filtering is carried out on a region except the visible light region by adopting a 14-point 2-degree polynomial;
and for the reflectivity spectrum after removing the noise, adopting an envelope removal method to obtain the reflectivity spectrum after removing the envelope;
the second acquisition unit is used for acquiring the reflectivity of a preset waveband corresponding to the type of the heavy metal to be detected according to the reflectivity spectrum; the preset wave band is obtained according to a characteristic spectrum of an active substance used for adsorbing and fixing the heavy metal to be detected in soil; the preset wave band is determined by the following steps: extracting a characteristic spectrum of the active substance of the heavy metal to be detected, and selecting a waveband by adopting a genetic algorithm to obtain the preset waveband; if the main adsorption and fixation substance of the heavy metal to be detected is an organic matter, the value ranges of the characteristic spectrum are a 410nm absorption peak and a 600-800nm absorption peak, and if the main adsorption and fixation substance of the heavy metal to be detected is a clay mineral, the value ranges of the characteristic spectrum are a 1400nm absorption peak, a 1900nm absorption peak and a 2200nm absorption peak;
and the calculating unit is used for calculating the content of the heavy metal to be detected in the soil by utilizing a pre-established soil heavy metal content estimation model according to the reflectivity, and the soil heavy metal content estimation model is established by adopting a partial least square algorithm.
5. The apparatus of claim 4, further comprising:
the third acquisition unit is used for acquiring the reflectivity spectrum of the soil sample and the content of the preset heavy metal of the soil sample before acquiring the reflectivity spectrum of the soil to be detected;
the fourth acquisition unit is used for acquiring a characteristic spectrum of an active substance used for adsorbing and fixing the preset heavy metal in the soil sample according to the reflectivity spectrum of the soil sample;
a fifth acquiring unit configured to acquire a reflectance of a wavelength band of the characteristic spectrum;
a selection unit for calculating the content of heavy metal by using a preset number of genetic algorithms according to the reflectivity, and selecting a group of wave bands with the smallest difference value with the content of the preset heavy metal;
and the construction unit is used for constructing the soil heavy metal content estimation model according to the reflectivity of the selected wave band and the content of the preset heavy metal.
6. An electronic device, comprising: a processor, a memory, a bus, and a computer program stored on the memory and executable on the processor;
the processor and the memory complete mutual communication through the bus;
the processor, when executing the computer program, implements the method of any of claims 1-3.
7. A non-transitory computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, implements the method of any one of claims 1-3.
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