KR101873003B1 - Detection of Heavy Metal in Soil Using hyperspectral Image and Spectral Angle Variables - Google Patents

Detection of Heavy Metal in Soil Using hyperspectral Image and Spectral Angle Variables Download PDF

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KR101873003B1
KR101873003B1 KR1020180041770A KR20180041770A KR101873003B1 KR 101873003 B1 KR101873003 B1 KR 101873003B1 KR 1020180041770 A KR1020180041770 A KR 1020180041770A KR 20180041770 A KR20180041770 A KR 20180041770A KR 101873003 B1 KR101873003 B1 KR 101873003B1
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spectral
data
image
spectral angle
angle
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KR1020180041770A
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권오섭
이금영
강성주
이민규
전의익
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(주)아세아항측
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    • G01—MEASURING; TESTING
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/24—Earth materials

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Abstract

The present invention relates to a method for detecting heavy metals in soil using hyperspectral images and a spectral angle variable. More specifically, the present invention generates spectroscopic angular images by inputting hyperspectral image data and reference spectral data obtained by a spectroscopic sensor. In the present invention, an absolute concentration of heavy metals in the soil is calculated by setting a relational expression for calculating an actual concentration to convert a generated spectral angle image to the absolute concentration. Accordingly, the present invention relates to the method for detecting heavy metals in soil using hyperspectral images and a spectral angle variable to detect heavy metals in the soil.

Description

TECHNICAL FIELD The present invention relates to a method for detecting heavy metals in a soil using ultraspectral imaging and spectral angle parameters,

The present invention relates to a method for detecting heavy metals in soil using ultrasound images and spectral angle variables, and more particularly, to a method for detecting heavy metals in soil by generating ultrasound image data and reference spectral data obtained by a spectroscopic sensor, And the detection of heavy metals in the soil using spectroscopic angular variables, which can detect the heavy metals in the soil by calculating the absolute concentration of the heavy metals in the soil by setting the relational formula for calculating the actual concentration to convert the spectral angle image into absolute density ≪ / RTI >

A hyperspectral sensor is a sensor that records electromagnetic waves reflected from an object or emitted by an object at several hundred or more consecutive spectral wavelengths.

Continuous spectral wavelength data obtained by the ultra - spectroscopic sensor can be used to detect various objects of the surface and vegetation.

Therefore, analyzing the spectral data recorded by the ultra-spectral sensor has an advantage that the kind of object can be easily identified.

At this time, the observed data can be stored in various forms such as a point format or a face format.

The reference spectral data is a spectral data that is used as a reference for the detection and concentration calculation of heavy metals. It refers to data that is stored as continuous spectral reflection values of electromagnetic waves reflected or emitted from a soil contaminated with a specific heavy metal.

In the case of soils containing heavy metals, it is composed of unique components for each type of heavy metal, so it is possible to detect heavy metals and to calculate concentration when using reference data defined by the kind or concentration of specific heavy metals together with the ultrasound data.

The spectral angle is a numerical value indicating the angle difference between the spectral value of the input image and the reference spectral data.

Spectral angle mapper (Spectral Angle Mapper) is used as a representative method to classify or search objects when detecting objects by using ultraspectral images.

The spectral angle mapper method uses the spectral angle as a detection parameter of similarity between two spectral data.

At this time, the spectral angle means a difference amount between the reference spectral vector and the angle obtained from the image, and the unit has a radian, which is well known in the art.

In the present invention, the numerical value of the spectral angle can be used as a measure of similarity between two spectral data and as a parameter of concentration calculation.

Heavy metals in soil are usually heavy metals, and heavy metals are heavier metals with a specific gravity of 4 or more. Typical examples are arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb).

On the other hand, heavy metal materials are leaked from waste metal mines, factory clusters, smelters, and abandoned mines.

The released heavy metals are exposed to the human body through air, drinking water, crops, etc. depending on the type of spillage, and can show acute toxicity and chronic toxicity according to the exposure.

In addition, due to the high persistence in the human body, even a trace amount of exposure dose does not remove the contaminants, which may lead to chronic diseases when exposed for a long period of time.

When using an ultra-spectral sensor, it can acquire spectral data through observation sensors in the room, on the ground, and in the air, and can detect heavy metals using observation data and reference data.

Soil pollution survey should be carried out on baseline and general survey of the target area to evaluate the contamination of soil, and the type of contaminants to be surveyed and sampling density should be considered according to soil pollution.

Accordingly, the present inventors have confirmed a method for detecting heavy metals using spectroscopic angles as a result of applying ultrasonic spectroscopic data and reference spectroscopic data to enable rapid identification of data in a wide range of indoor, ground and air environments. It was completed.

Korea Patent Registration No. 10-1760474 (Jul. 17, 2017) 'Progress of Ultrasound Image Processing for Detecting Soil Hot Spots Containing Heavy Metals'

SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned problems in the prior art, and an object of the present invention is to provide a spectroscopic angular image generating apparatus and method, Providing a method for detecting heavy metals in soils using spectroscopic images and spectroscopic angular variables to detect heavy metals in soil by calculating the absolute concentration of heavy metals in soil by setting relational formula for calculation of actual concentration to convert image to absolute concentration There is a main purpose in.

The present invention provides a means for achieving the above object by providing an enclosure 300 and a processing unit 200 mounted inside the enclosure 300 at a specific point among the detection target areas acquired by the ultra- A first step (101) of inputting superspectral image data, reference spectral data and actual density data; A second step (103) of generating a spectral angle image using the ultrasound image data of which conformity is passed among the data inputted in the first step (101); A third step (104) of confirming the correlation required for the linear regression analysis to convert the generated spectral angle image into absolute density; A fourth step (105) of calculating an absolute density from the slope information obtained from the linear regression equation using the data for which correlation has been confirmed; And a fifth step (107) of comparing the actual concentration detected at the specific point with the calculated absolute concentration to evaluate the accuracy of heavy metal detection, the method comprising the steps of: (a) detecting a heavy metal in soil by using an ultrasound image and a spectral angle parameter;

The processing unit 200 includes a control unit 210 as a microprocessor, an input unit 220 connected to the control unit 210 to input actual density data including ultraspectral image data and reference spectral data, An arithmetic unit 230 for calculating a spectral angle by calculating an angular difference between a reference spectral vector of the reference spectral data and a superspectral image vector of the superspectral image data according to a control signal of the control unit 210, A spectral angle image generating unit 240 for generating a spectral angle image based on the calculated spectral angle, a memory unit 250 for storing data input in accordance with a control signal of the control unit 210, A correlation checking unit 260 that uses only a correlation index determined according to a control signal of the controller 210 as a linear regression analysis data, An absolute density calculating unit 270 for calculating an absolute density in the slope information obtained from the linear regression equation using data adopted through the controller 210 and an absolute density calculating unit 270 for calculating an absolute density and an actual density calculated in accordance with the control signal of the controller 210 And an output unit 290 for outputting the absolute density including the estimated accuracy according to the control signal of the control unit 210 to a screen, a memory stick, or a printer ;

In the second step 103, the angle difference between the spectroscopic value of the superspectral image data and the spectral value of the reference spectroscopic data is checked using a known spectral angle mapper, and only the values within the range of 0 to 1 An effective spectral angle confirming process 103a for selecting an angle, and a spectral angle image output process 103b for outputting a spectral angle image by inputting the selected effective spectral angle value step by step;

The third step 104 includes a spectral angle parameter input step 104a for displaying a spectral angle value to each pixel of the spectral angle image, and a correlation process using only a pixel having a correlation index of 0.9 or more in the linear regression analysis on the display value An index adoption process 104b;

The fourth step 105 includes a band calculation step 105a for calculating a slice value according to the slope of the linear regression equation according to the linear regression method for each pixel of the spectral angle image as an absolute density, And an absolute density image outputting step (105b) of outputting an absolute density image in a state in which each pixel of the spectral angle image is displayed;

The fifth step 107 includes a measured value concentration display process 107a for selecting and displaying the maximum value and the minimum value representing the high and low concentrations of the actual concentration data, And a measured value comparison reading process (107b) for evaluating the degree of similarity by comparing pixel difference differences and confirming the accuracy. The present invention also provides a method for detecting heavy metals in soil using spectroscopic images and spectral angle variables.

According to the present invention, a spectroscopic angular image is generated by inputting superspectral image data and reference spectral data acquired by a spectroscopic sensor, and a relational expression for estimating the actual concentration is set in order to convert the generated spectroscopic angular image into absolute density, By calculating the absolute concentration of heavy metals, it is possible to detect the heavy metals in the soil.

1 is an exemplary flowchart of a method for detecting heavy metals in soil using ultrasound image and spectral angle parameter according to an embodiment of the present invention.
Figure 2 is an exemplary block diagram of a processing unit according to the present invention.
Figs. 3 (a) -3 (d) are diagrams illustrating an example of a housing in which a processing unit according to the present invention is mounted.
FIG. 4 is a diagram showing the shape of a superspectral image and the shape of a spectral angle image at a data input step according to a method of detecting heavy metals in soil using an ultrasound image and a spectral angle parameter according to an embodiment of the present invention.
FIG. 5 illustrates shapes of final products generated according to a method for detecting heavy metals in soil using ultrasound images and spectral angle variables according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Before describing the present invention, the following specific structural or functional descriptions are merely illustrative for the purpose of describing an embodiment according to the concept of the present invention, and embodiments according to the concept of the present invention may be embodied in various forms, And should not be construed as limited to the embodiments described herein.

In addition, since the embodiments according to the concept of the present invention can make various changes and have various forms, specific embodiments are illustrated in the drawings and described in detail herein. However, it should be understood that the embodiments according to the concept of the present invention are not intended to limit the present invention to specific modes of operation, but include all modifications, equivalents and alternatives falling within the spirit and scope of the present invention.

Prior to the description of the present invention, the ultrasound sensor, ultrasound image data, reference spectroscopic data, spectroscopic data, etc., which are used in the present invention, are already known in the art, And data obtained from these data. Therefore, the description of their own concept or acquisition process is replaced with the level mentioned in the prior art, and the following description will focus on the heavy metal detection method using them.

General soil investigation methods include precision instrument analysis by soil test process method and simple portable X-ray and laser wave method. However, in case of instrument analysis method, sampling is performed directly and physicochemical treatment of the object is performed It takes a lot of time and money.

In addition, there is a simple portable type method, but it is available only to users who are allowed to use the equipment strictly by the use of radiation, and X-ray and the like have disadvantages in that they can not be observed or collected in the air.

Accordingly, the present invention can quickly identify the soil contamination by heavy metals and estimate the actual concentration, thereby making it possible to increase the promptness and economical efficiency of pollution investigation.

In other words, it monitors the heavy metals by interpreting the supersonic spectral image data obtained from the ground or air, so there is no physicochemical conversion process for the object and it is easy to acquire the data, which makes it easy to acquire and interpret data quickly.

Particularly, monitoring method using ultrasonic image by drone or airplane enables to distinguish between precise survey area and general survey area by screening first the area that needs to be surveyed before the survey of the soil pollution survey, It then enables the estimation of the concentration of soil between analysis points.

In addition, it is possible to increase the economical efficiency of soil heavy metal pollution survey by direct sampling according to the field survey, and it is possible to measure quickly even in case of difficult terrain.

In addition, it has the advantage of monitoring the heavy metals in the soil of the whole area, not the distributional performance of the target area, and it is easy to understand the pollution behavior when time series data are constructed.

In addition, the model equation described below means an equation used in a known linear regression analysis method, and the term " input " means that the data used for the linear regression analysis is key-in.

As shown in FIG. 1, the present invention having the above advantages is a first step 101 for inputting hyperspectral image data, reference spectral data and actual density data for a specific point among the detection target areas acquired by the ultra-spectral sensor ); A second step (103) of generating a spectral angle image using the ultrasound image data of which conformity is passed among the data inputted in the first step (101); A third step (104) of confirming the correlation required for the linear regression analysis to convert the generated spectral angle image into absolute density; A fourth step (105) of calculating an absolute density from the slope information obtained from the linear regression equation using the data for which correlation has been confirmed; And a fifth step (107) of comparing the actual concentration detected at the specific point with the calculated absolute concentration to evaluate the accuracy of heavy metal detection.

At this time, the present invention includes a processing unit 200 as shown in FIG. 2 for the above-described step processing, and the processing unit 200 is mounted in a server rack in the enclosure 300 as shown in FIG.

The processing unit 200 includes a control unit 210 that is a microprocessor, an input unit 220 connected to the control unit 210 and configured to input measured concentration data including ultraspectral image data and reference spectral data, An operation unit 230 for calculating a spectral angle by calculating an angle difference between a reference spectral vector of the reference spectral data and an ultrasound image vector of the ultraspectral image data according to a control signal of the control unit 210, A spectral angle image generator 240 for generating a spectral angle image based on the spectral angle calculated according to the control signal of the controller 210, A correlation checking unit 260 that uses only a correlation coefficient determined according to a control signal of the controller 210 as a linear regression analysis data, An absolute density calculating unit 270 for calculating an absolute density in the slope information obtained from the linear regression equation using the data adopted through the confirming unit 260 and an absolute density calculating unit 270 for calculating an absolute density based on the absolute density calculated in accordance with the control signal of the control unit 210 An output unit 290 for outputting the absolute density including the estimated accuracy according to the control signal of the control unit 210 to a screen or a memory stick or a printer, .

In the first step 101, only the ultrasound image data for a specific point arbitrarily set in the ultrasound image data for the detection target is selected through the input unit 220, .

The reason why only a specific point is selected and input is to check the accuracy of heavy metal detection through the fifth step 107. In the fifth step 107, The accuracy of the measurement is compared with the measurement value according to the present invention, and when the accuracy is high, the heavy metal detection for the entire detection target area is converted into the measurement value according to the present invention rather than the actual measurement.

Therefore, it can be understood that the present invention is intended to evaluate whether switching to such a measurement value is possible, and to enable heavy metal detection by measurement without actual measurement.

In addition, the reference spectral data is a table value of spectral reflection values determined for each heavy metal concentration in a soil contaminated with a specific heavy metal, as mentioned in the related art. Therefore, the reference spectral data is a reference value and is a known value.

In addition, after the first step 101, the conformance checking step 102 may be further performed.

The conformance checking step 102 is a step for checking whether the spectral broadband of the ultrasound image data and the actual density data are overlapped and whether the measurement unit is correct, and minimizing the error.

The second step 103 includes a step 103a for checking an effective spectral angle to generate a spectral angle image, and a step 103b for outputting a spectral angle image.

At this time, in the process 103a for checking the effective spectral angle, the angle difference between the spectral value of the superspectral image data and the spectral value of the reference spectral data is checked using a known spectral angle mapper, Only the values within the range are selected as effective spectral angles.

Also, the process 103b of outputting the spectral angle image is a process of outputting the spectral angle image by inputting the selected effective spectral angle value step by step, thereby generating the spectral angle image.

In addition, the third step 104 includes a spectral angle parameter input step 104a for displaying a spectral angle value to each pixel of the spectral angle image, and a spectral angle parameter input step 104a for selecting only pixels having a correlation index of 0.9 or more in the linear regression analysis on the displayed values (104b). ≪ / RTI >

In this case, it is preferable that the spectral angle is input in the spectral angle parameter input step 104a, and the corresponding spectral angle is recorded for the detected pixels among all the pixels of the ultrasound image data,

In addition, although the setting of the correlation index can be changed in the correlation index adoption process 104b, the correlation index of 0.9 or more is adopted in the present invention in order to increase the accuracy of the analysis.

The fourth step 105 includes a band operation step 105a for calculating a slice value according to the slope of the linear regression equation according to the linear regression method for each pixel of the spectral angle image as an absolute density, And an absolute density image output process 105b for outputting an absolute density image in a state in which the absolute density is displayed on each pixel of the spectral angle image.

Here, the error checking step 106 may be further performed after the fourth step 105.

The error checking step 106 is a step of checking whether a pixel has a value of 0 to exclude the absolute density image because a value of 0 can be outputted as an absolute density image by calculation.

Finally, the fifth step 107 includes a measured value concentration display process 107a for selecting and displaying the maximum value and the minimum value representing the high and low concentrations of the measured concentration data, and the displayed measured value concentration and the calculated absolute concentration to 1 : 1), and a measured value comparison readout process (107b) for checking the accuracy of the similarity degree by comparing the pixel difference.

At this time, standard deviation, standard error and the like may be used for comparison reading, and the reference value of similarity may be changed depending on the setting.

Thus, if it is determined that the accuracy is equal to or higher than the set value, the heavy metal detection method according to the present invention is applied to the entire detection target area.

In order to confirm the possibility of the detection method according to the present invention as described above, the process as shown in FIG. 4 has been able to detect within a fairly accurate error range.

Fig. 4 (a) shows the shape of a soil object contaminated with heavy metals, Fig. 4 (b) shows an actual measurement value in a soil object contaminated with heavy metals, (D) shows a spectroscopic angular image formed by using ultraspectral image data and reference wavelength data to detect heavy metals, and FIG. 5 .

In addition, the form of the final product produced according to the present invention can be illustrated as shown in FIG.

On the other hand, the housing 300 is slidably mounted on a rail R as shown in FIG. 3A, and the rail R is installed at a position spaced apart from the mounting surface by a predetermined height.

Therefore, the housing 300 can be protected from vibration or the like on the installation surface, and the housing 300 can be slidably moved, thereby improving workability in maintenance and replacement of parts (cards).

At this time, a door DR is installed on the front surface of the housing 300, and a MAG is installed on both sides and the bottom surface of the housing 300 to have air permeability, thereby allowing natural cooling.

3B, the MAG may include a reference chamber 310 and a mesh-type holster (MSS) composed of a bead-shaped hair 320 bundled at a predetermined interval in the reference chamber 310 Lattice weave.

Particularly, the reference chamber 310 uses a nylon filament, and the bead-shaped hair 320 has a bundle thread made of stretchable nylon stretch yarn of 140 deniers or less, Is tied to the reference chamber (310), and then both sides are swollen by being cut to have a spherical shape.

By doing so, dusts such as dust and foreign matter can be vented in a filtered state, thereby contributing to prevention of deterioration by enhancing the natural cooling ability of parts mounted inside.

In the process of forming the net-like saccharification thread (MSS), a bundle yarn (YAN) for forming bead-shaped hairs (320) progresses in parallel with three strands of the reference yarn (310) The adhesive agent is applied to a part of the surface of the reference chamber 310 through the adhesive agent damper 330.

The bundling thread feeder 340 moving in a direction orthogonal to the advancing direction of the reference yarn 310 intersects with each other at the bundle position while being moved while crossing the reference yarn 310, And threaded into the thread 310.

At this time, the twisted bundle yarn (YAN) is completely adhered to the reference chamber 310 in a twisted state by an adhesive.

When the bundle yarn YAN is completely bundled with the reference yarn 310 in a twisted state, the bundle yarns YAN of the reference yarn 310 are cut with the cutter.

Since the bundle yarn (YAN) is formed of 140 strands, spherical beads are formed as they are unfolded. When the mesh chains (MSSs) are arranged at certain intervals and are bound to the net crane TL, The diameter of the beads becomes a kind of gap, and the beaded hairs 320 are blocked in an unfolded state therebetween.

In order to enhance the durability of the nylon filament, that is, the reference chamber 310, the reference chamber 310 includes 8.0 wt% of sorbitan olivate, 2.5 wt% of hydroxyproline, 15% by weight of butyl prop-2-propenoate, 10% by weight of urosolic acid, 2.5% by weight of isohexadecane, 5.0% by weight of cyanoacrylate, It is preferable to use it in the impregnation solution containing 4.5 wt% of calcined ytterbium oxide, 1.5 wt% of alumina and the remaining polyethylene resin.

At this time, the sorbitan olivate is added as a monoester of fatty acid derived from sorbitol-derived hexitolanhydride and olive oil to enhance the natural emulsification function due to the enhancement of the surfactant, and the hydroxyproline is added to gelatin hydrolyzate , It is added for the purpose of increasing the flexibility of the resin composition while being gelatinized.

The butyl-2-enoate is added to increase the bending strength while providing ductility, impact reinforcement and binding property to the composition, and the uronic acid is a powder having excellent needle-like absorbability and adsorbs the polymer particles on the surface of the inorganic material Is added to improve bufferability, bending properties and durability and crack resistance.

In addition, the isohexadecane is added so as to contribute to separation and removal of foreign substances adhering to the surface of the molded article, and the cyanoacrylate is added to suppress the high thermal expansion and to enhance the binding force between the compositions .

In addition, the sintered ytterbium oxide is added as a rare earth metal to increase the chemical resistance and chemical resistance while increasing the durability through grain refinement, and the alumina is added as an oxide of aluminum in order to improve the heat resistance.

The polyethylene resin is a base resin. This configuration enables efficient cooling.

A rail groove RV is formed in the rail R and a slider 400 is inserted in the rail groove RV. The slider 400 includes an upper body 410 and a lower body 420 .

The reason for this separation is that the housing 300 can be separated and assembled easily and quickly. That is, the bolt is constructed not of a loosening method but of a detachable type. The reason for this is that the housing 300 according to the present invention is not made of a metal body, but is made of a lightweight synthetic resin.

For this, a predetermined depth insertion slit 412 is formed from the lower end surface of the upper body 410, and the insertion piece 422 is inserted into the insertion piece fixing slit 412.

A through hole 414 is formed at one side of the upper body 410 in a direction perpendicular to the insertion piece fixing slit 412 so as to communicate with the insertion piece fixing slit 412. The through hole 414 Is formed with a predetermined depth releasing hole 414a from the outer surface of the upper body 410 toward the insertion piece fixing slit 412 and is connected to the releasing hole 414a so that a larger diameter fixing hole 414b Are formed.

In this case, the fixing hole 414b is formed to be slightly larger than the protrusion 433.

The insertion piece 422 protrudes from the upper surface of the lower body 420 to a thickness smaller than that of the lower body 420 and a mounting hole 424 penetrating through the insertion piece 422 in the thickness direction The elastic fixing unit 430 is screwed to the mounting hole 424 and the lower end surface of the lower body 420 constitutes a flange portion FLT fixed to the upper surface of the housing 300 by bolts.

As shown in FIGS. 3C and 3D, the elastic fixing unit 430 includes a fixture 431 having a 'C' shape in which a thread is formed on a peripheral surface thereof and is screwed to the mounting hole 424, Having a protruding guide hole 432 formed through the center of the closed surface of the fixture 431 and a protrusion 433 protruding through the protruding guide hole 432, A coil spring 435 urged on the rear surface of the elastic flow hole 434 and a spring fixed to the open end of the fixing hole 431 while pressing the coil spring 435. [ (436).

At this time, the spring fixing piece 436 is formed in a shape of 'ㅕ', and one end of the coil spring 435 can be more stably fixed by having the spring receiving groove 437 at the center of one side, The tool groove 438 is further formed so as to be rotatably operated by using a tool, such as a screwdriver.

The protrusion 433 is configured to be easily pushed even when the tip is rounded and brought into contact with the angled portion and the elastic fixture 354 integrally formed with the protrusion 433 is inserted into the fixture 431 from the coil spring 435 So that the lower body 420 can be easily clamped and unclamped.

Thus, when the insertion piece 422 of the lower body 420 is inserted into the insertion piece fixing slit 412 of the slider 400, the projection 433 is elastically compressed and then inserted into the fixing hole 414b The protruding portion 433 is protruded by the urging force of the instantaneous coil spring 435 and is caught by the fixing hole 414b so that the insertion piece 422 is fixed to the insertion piece fixing slit 412 at that moment.

Accordingly, the lower body 420 can be easily and quickly attached to and detached from the upper body 410, thereby enhancing convenience of operation.

On the other hand, when it is desired to release the lower body 420, a means such as a pin is inserted into the releasing hole 414a and pressed.

The coil spring 435 is compressed while the protrusion 433 is pressed and the protrusion 433 is separated from the fixing hole 414b so that when the lower body 420 is pulled downward, Can be easily separated.

101: first step 103: second step
104: third step 105: fourth step
107: Step 5

Claims (1)

The ultrasound image data, the reference spectral data, and the actual density data of the specific region of the detection target region acquired by the ultrasound spectral sensor using the enclosure 300 and the processing unit 200 mounted in the enclosure 300 A first step (101) of inputting an input signal; A second step (103) of generating a spectral angle image using the ultrasound image data of which conformity is passed among the data inputted in the first step (101); A third step (104) of confirming the correlation required for the linear regression analysis to convert the generated spectral angle image into absolute density; A fourth step (105) of calculating an absolute density from the slope information obtained from the linear regression equation using the data for which correlation has been confirmed; And a fifth step (107) of comparing the actual concentration detected at the specific point with the calculated absolute concentration to evaluate the accuracy of heavy metal detection, the method comprising the steps of: (a) detecting a heavy metal in soil by using an ultrasound image and a spectral angle parameter;
The processing unit 200 includes a control unit 210 as a microprocessor, an input unit 220 connected to the control unit 210 to input actual density data including ultraspectral image data and reference spectral data, An arithmetic unit 230 for calculating a spectral angle by calculating an angular difference between a reference spectral vector of the reference spectral data and a superspectral image vector of the superspectral image data according to a control signal of the control unit 210, A spectral angle image generating unit 240 for generating a spectral angle image based on the calculated spectral angle, a memory unit 250 for storing data input in accordance with a control signal of the control unit 210, A correlation checking unit 260 that uses only a correlation index determined according to a control signal of the controller 210 as a linear regression analysis data, An absolute density calculating unit 270 for calculating an absolute density in the slope information obtained from the linear regression equation using data adopted through the controller 210 and an absolute density calculating unit 270 for calculating an absolute density and an actual density calculated in accordance with the control signal of the controller 210 And an output unit 290 for outputting the absolute density including the estimated accuracy according to the control signal of the control unit 210 to a screen, a memory stick, or a printer ;
In the second step 103, the angle difference between the spectroscopic value of the superspectral image data and the spectral value of the reference spectroscopic data is checked using a known spectral angle mapper, and only the values within the range of 0 to 1 An effective spectroscopic angle confirmation step 103a for selecting an angle, and a spectroscopic angle image output step 103b for outputting a spectroscopic angle image by inputting the selected effective spectroscopic angular value step by step;
The third step 104 includes a spectral angle parameter input step 104a for displaying a spectral angle value to each pixel of the spectral angle image, and a correlation process using only a pixel having a correlation index of 0.9 or more in the linear regression analysis on the display value An index adoption process 104b;
The fourth step 105 includes a band calculation step 105a for calculating a slice value according to the slope of the linear regression equation according to the linear regression method for each pixel of the spectral angle image as an absolute density, And an absolute density image outputting step (105b) of outputting an absolute density image in a state in which each pixel of the spectral angle image is displayed;
The fifth step 107 includes a measured value concentration display process 107a for selecting and displaying the maximum value and the minimum value representing the high and low concentrations of the actual concentration data, And a measured value comparison reading process (107b) for evaluating the similarity degree by comparing the pixel relation difference and confirming the accuracy. The method for detecting heavy metals in soil using the ultrasonic spectroscopic image and the spectral angle variable.
KR1020180041770A 2018-04-10 2018-04-10 Detection of Heavy Metal in Soil Using hyperspectral Image and Spectral Angle Variables KR101873003B1 (en)

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CN109738380A (en) * 2019-01-25 2019-05-10 西北农林科技大学 A kind of high-spectrum remote-sensing judgment method of soil salinization degree
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CN113791040A (en) * 2021-07-20 2021-12-14 广州华清环境监测有限公司 Soil heavy metal detection method and system
CN113902717A (en) * 2021-10-13 2022-01-07 自然资源部国土卫星遥感应用中心 Satellite-borne hyperspectral farmland bare soil target identification method based on spectrum library
KR20230033869A (en) * 2021-09-02 2023-03-09 한국과학기술연구원 Method for monitoring soil with stabilizer
CN116046692A (en) * 2023-03-23 2023-05-02 航天宏图信息技术股份有限公司 Soil heavy metal pollution monitoring method and device based on hyperspectrum
CN116199284A (en) * 2023-02-20 2023-06-02 济南森华工程技术有限公司 Power plant wastewater zero discharge system

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109738380A (en) * 2019-01-25 2019-05-10 西北农林科技大学 A kind of high-spectrum remote-sensing judgment method of soil salinization degree
CN109738380B (en) * 2019-01-25 2022-09-30 西北农林科技大学 Hyperspectral remote sensing judgment method for soil salinization degree
KR102235741B1 (en) * 2019-12-02 2021-04-02 주식회사 도영이앤지 Safety analysis system using multispectral image information and GPS
CN113791040A (en) * 2021-07-20 2021-12-14 广州华清环境监测有限公司 Soil heavy metal detection method and system
KR20230033869A (en) * 2021-09-02 2023-03-09 한국과학기술연구원 Method for monitoring soil with stabilizer
KR102565484B1 (en) 2021-09-02 2023-08-11 한국과학기술연구원 Method for monitoring soil with stabilizer
CN113902717A (en) * 2021-10-13 2022-01-07 自然资源部国土卫星遥感应用中心 Satellite-borne hyperspectral farmland bare soil target identification method based on spectrum library
CN116199284A (en) * 2023-02-20 2023-06-02 济南森华工程技术有限公司 Power plant wastewater zero discharge system
CN116199284B (en) * 2023-02-20 2023-11-14 济南森华工程技术有限公司 Power plant wastewater zero discharge system
CN116046692A (en) * 2023-03-23 2023-05-02 航天宏图信息技术股份有限公司 Soil heavy metal pollution monitoring method and device based on hyperspectrum

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