CN113010996B - Method and system for extracting radon concentration abnormity based on entropy-median filtering in sub-region - Google Patents

Abstract

A method for extracting a soil radon concentration abnormal region based on information entropy-subregion median liner value filtering relates to the technical field of uranium mine resource exploration, combines information entropy and subregion median liner value filtering image processing technology, constructs a functional relation model, determines parameter values of large and small windows according to the functional relation model, uses large and small windows corresponding to the parameter values to perform subregion median liner value filtering processing on a soil radon concentration contour map, and extracts a corresponding soil radon concentration abnormal region according to a subregion median liner value filtering result; the invention also provides a system for extracting the soil radon concentration abnormal region based on the information entropy-median liner value filtering in the subarea. The method avoids the problem that the result of processing the subzone median value filtering data is uncertain due to human experience factors, ensures the accuracy of the subzone median value filtering on identifying abnormal areas, and can provide valuable reference basis for delineating the exploration position and the mineralization range of the uranium mine.

Description

Method and system for extracting radon concentration anomalies based on entropy-subregion median filtering

technical field

The invention relates to the technical field of uranium ore exploration, in particular to a method and a system for extracting abnormal areas of soil radon concentration based on information entropy-sub-area median contrast value filtering.

Background technique

With the increasing proportion of nuclear energy in the energy structure, the demand for uranium resources is increasing, which promotes the exploration and development of uranium resources.

The sub-area median contrast filtering method is a chemical exploration data processing method established on the basis of EDA (exploration data analysis) technology and filtering technology without processing the original data and extracting abnormal information. It is commonly used in 1:20 10,000, 1:50,000 and other metal deposits are extracted from weak geochemical anomalies. It should be pointed out that the data processing of the sub-region median contrast filter method (SAMCF) requires high work experience. To a large extent, based on the experience of the staff, the distribution range of positive and negative anomalies obtained by the median contrast filtering method of sub-regions under different large and small window conditions is different, and there is uncertainty in the area of abnormal information extraction.

SUMMARY OF THE INVENTION

One of the purposes of the present invention is to provide a method for extracting abnormal areas of soil radon concentration based on information entropy-sub-area median contrast value filtering, so as to solve the problem of inconsistencies in abnormal information extraction areas caused by relying on work experience to select large and small window parameters. Certainty issues.

In order to solve the above-mentioned technical problems, the present invention adopts the following technical scheme: based on the information entropy-sub-area median contrast value filtering to extract the method for the abnormal area of soil radon concentration, the following formula is used to determine the medium and small values of the sub-area median contrast value filtering. Window parameter ratio:

S(m/n)| _min =∫ _m/n (-1)×P(i)×lnP(i) (1);

In formula (1), m is the parameter value of the large window, n is the parameter value of the small window, m/n is the parameter ratio of the large and small windows, and S(m/n)| _min is the parameter ratio of the large and small windows. is the minimum information entropy value of the median-contrast filter image of the corresponding sub-area when m/n, P(i) is the corresponding sub-area under the condition that the large window parameter value is m and the small window parameter value is n The probability of the occurrence of the i-th grayscale after the grayscale of the bit-contrast filter image;

Determine the parameter values of the large and small windows according to the parameter ratio determined by the formula (1), and use the large and small windows corresponding to the parameter values to filter the median line value of the soil radon concentration contour map in the sub-region. , and extract the corresponding abnormal area of soil radon concentration according to the filtering result of the median lining value of the sub-area.

Further, it also includes the steps of measuring soil radon concentration and forming a soil radon concentration contour map.

Further, it also includes the use of a large window with a parameter value of m and a small window with a parameter value of n to filter the sub-region median contrast value of the soil radon concentration contour map and filter the sub-region median contrast value. The result is a grayscale processing step.

Further, after the corresponding sub-area median-contrast filtering result is grayscaled, the step of determining the occurrence probability of the i-th grayscale after the sub-area median-contrast filtering image is grayscaled.

Further, the probability of occurrence of the i-th grayscale after grayscale of the corresponding sub-region median-contrast filter image is determined by the following formula:

P(i)=N(i)/N(2);

In formula (2), P(i) is the probability of the occurrence of the i-th grayscale of the median-contrast filter image of the sub-region after graying; N(i) is the gray-scale of the median-contrast filter image of the sub-region. The total number of occurrences of the i-th grayscale after the grayscale; N is the total number of pixels after grayscale of the median-contrast filter image in this sub-region.

Based on the above-mentioned method for extracting abnormal areas of soil radon concentration, the present invention also proposes a system for extracting abnormal areas of soil radon concentration based on information entropy-sub-area median contrast filtering. The adopted technical scheme is as follows, and the system includes:

The parameter determination unit calculates the parameter ratio of the large and small windows in the median contrast filter of the sub-region as follows, and determines the parameters of the large and small windows according to the calculated parameter ratio:

S(m/n)| _min =∫ _m/n (-1)×P(i)×lnP(i) (1);

In formula (1), m is the parameter value of the large window, n is the parameter value of the small window, m/n is the parameter ratio of the large and small windows, and S(m/n)| _min is the parameter ratio of the large and small windows. is the minimum information entropy value of the median-contrast filter image of the corresponding sub-area when m/n, P(i) is the corresponding sub-area under the condition that the large window parameter value is m and the small window parameter value is n The probability of the occurrence of the i-th grayscale after the grayscale of the bit-contrast filter image.

The image processing unit, according to the large and small window parameters determined by the parameter determination unit, uses the large and small windows corresponding to the parameters to filter the subregion median contrast value of the soil radon concentration contour map and output the subregion. District median contrast filter results.

The abnormal area extraction unit extracts the corresponding abnormal area of soil radon concentration according to the filtering result of the median contrast value of the sub-area output by the image processing unit.

The image processing unit is also used to filter the sub-area median contrast value of the soil radon concentration contour map by using a large window with a parameter value of m and a small window with a parameter value of n, and perform the corresponding sub-area median contrast value filtering processing. The value filtering result is grayscaled.

The parameter determining unit is further configured to determine the probability of the occurrence of the i-th grayscale of the sub-region median contrast value filtered image after grayscale processing of the subregion median contrast value filtering result by the image processing unit.

The parameter determination unit determines the probability of occurrence of the i-th grayscale after the grayscale of the corresponding sub-area median-contrast filter image is as follows:

P(i)=N(i)/N(2);

In formula (2), P(i) is the probability of the occurrence of the i-th grayscale of the median-contrast filter image in the sub-region after grayscale; N(i) is the median-contrast-filtered image gray in the sub-region. is the total number of occurrences of the i-th grayscale after the grayscale; N is the total number of pixels after grayscale of the median-contrast filter image in this sub-region.

The beneficial effects obtained by the present invention are as follows: the present invention regards the entire measurement area as an information source that provides multiple information, combines the information entropy with the sub-area median contrast filter image processing technology, and constructs the sub-area median The functional relationship model between the ratio of large and small windows and the image entropy in the contrast filter, the parameter ratio of the optimal large and small windows in the median contrast filter of the subregion can be calculated through the functional relationship model, and the subsequent subregions can be determined accordingly. This method avoids the problem of uncertainty in the data processing results of the median contrast value filtering in the sub-area caused by human experience factors, and ensures the median of the sub-area. The accuracy of the contrast filter to identify abnormal areas can provide a valuable reference for delineating the exploration location and mineralization range of uranium deposits.

Description of drawings

Figure 1 is a contour map of soil radon concentration in the measurement area;

Fig. 2 is the filtering result of median contrast value of soil radon concentration sub-area in measurement area under different window parameter conditions in the embodiment;

Fig. 3 is the relation diagram of different difference value method and image entropy value in the embodiment;

Fig. 4 is a grid diagram of soil radon concentration under different difference methods in the embodiment;

FIG. 5 is a graph showing the relationship between the parameter ratio of large and small windows and the image information entropy in the embodiment.

Detailed ways

In order to facilitate the understanding of those skilled in the art, the present invention will be further described below with reference to the embodiments and the accompanying drawings, and the contents mentioned in the embodiments are not intended to limit the present invention.

Compared with the prior art, the improvement of the present invention mainly lies in that the whole measurement area is regarded as an information source that provides multivariate information, and the information entropy is combined with the sub-area median contrast filtering image processing technology to construct a sub-area The functional relationship model between the ratio of large and small windows and the image entropy value in the median contrast filter, and the parameter ratio of the optimal large and small windows in the median contrast filter of the sub-region is calculated through the functional relationship model, and is determined from this. The large and small window parameter values in the subsequent sub-region median contrast value filtering processing, so as to avoid the problem of uncertainty in the sub-region median contrast value filtering data processing results caused by human experience factors.

The above improvements will be described in detail below.

Different from the prior art, the method for extracting abnormal areas of soil radon concentration based on information entropy-sub-area median contrast value filtering according to the present invention is to determine the ratio of large and small window parameters in the sub-area median contrast value filtering according to the following formula: :

S(m/n)| _min =∫ _m/n (-1)×P(i)×lnP(i) (1);

In the above formula (1), m is the parameter value of the large window, n is the parameter value of the small window, m/n is the parameter ratio of the large and small windows, and S(m/n)| _min is the parameter of the large and small windows When the ratio is m/n, the minimum information entropy value of the median-contrast filter image of the corresponding sub-area, P(i) is the parameter value m of the large window and the parameter value of the small window is the corresponding sub-area under the condition of n The probability of occurrence of the i-th grayscale after the grayscale of the bit-contrast filter image; then determine the parameter values of the large and small windows according to the parameter ratio determined by the formula (1), and then use the large and small windows corresponding to the aforementioned parameter values. In the small window, the contour map of soil radon concentration is filtered by the median contrast value of the sub-region, and finally the corresponding abnormal area of soil radon concentration is extracted according to the filtering result of the median contrast value of the sub-region.

The general idea of the above scheme for extracting abnormal areas of soil radon concentration based on information entropy-sub-area median contrast value filtering is as follows: the same as the existing sub-area median contrast value filtering method, first of all, it is necessary to delineate the measurement area and measure Soil radon concentration in this area, and form a contour map of soil radon concentration in the measurement area. Then, multiple groups of large and small windows with different parameter ratios (the parameter of the large window is m×m, and the parameter of the small window is n×n) are used to filter the median line value of the soil radon concentration contour map in the sub-region. The corresponding sub-area median-contrast filtering results are processed to grayscale, and then the probability of the i-th grayscale appearing after the corresponding sub-area median-contrast filtering image is grayscaled is determined.

The above-mentioned i-th grayscale is determined according to the actual exploration requirements. Specifically, the occurrence probability of the i-th grayscale after the grayscale of the median contrast filter image of the sub-region is calculated by the following formula, P(i)=N( i)/N; in the formula, P(i) is the probability of the occurrence of the i-th grayscale after the grayscale of the median-contrast filter image in the sub-area; N(i) is the median-contrast filter of the sub-area The total number of occurrences of the i-th grayscale after the image is grayed; N is the total number of pixels after the grayscale of the median-contrast filter image in this sub-area.

Then, with reference to the functional relationship model of formula (1), when the ratio of the parameters of the large and small windows is m/n, and the corresponding sub-area median contrast filter image is grayscaled, the probability of the ith level grayscale appearing is P(i) According to the relationship between the image information entropy value and the ratio m/n of the large and small window parameters, determine the optimal (when the image information entropy value is the smallest) the parameter ratio of the large and small windows, and then determine the large and small windows. Small window parameter value. Next, use the large and small windows corresponding to the aforementioned parameter values to filter the sub-region median contrast value again on the soil radon concentration contour map and output the final sub-region median contrast value filtering result.

Finally, according to the filtering result of the final sub-area median lining value, the corresponding abnormal area of soil radon concentration is extracted from it. It should be pointed out that, in the present invention, the method of extracting the abnormal area of soil radon concentration from the filtering result of the median value of the sub-area has no substantial change compared with the prior art. Repeat.

The present invention will be further described below in conjunction with specific examples, first delineate an area with a length of 8km and a width of 7.5km as a measurement area, and then adopt the electrostatic collection method RAD7 type alpha energy spectrum radon gas detector to measure the field soil radon concentration in the measurement area, The distance of the survey line is 500m, the distance of the measurement point is 100m, and the soil depth is 70cm, forming a contour map of soil radon concentration as shown in Figure 1.

The sub-area median contrast filtering method is developed on the basis of EDA (exploration data analysis) technology and filtering technology. It is based on robust statistics and does not require any processing of the original data to extract abnormal geochemical exploration data processing methods. , the formula is as follows:

F _u =Q _u +1.5S _h

F ₁ =Q ₁ -1.5S _h

C _{F, P} = M _Wc /F _u

C _F,N =M _Wc /F ₁

In the formula: F _u represents the lower limit of abnormal points; F ₁ represents the upper limit of abnormal points; Q _u represents the upper 4 points; Q ₁ represents the lower 4 points, and _Sh represents the inner divergence. When _{CF, P} > 1, it is a positive abnormality; when _{CF, N} <1, it is a negative abnormality.

First, use multiple sets of large and small windows with different parameter ratios (large window parameters are 6 × 6, 9 × 9, and small window parameters are 2 × 2, 3 × 3) to compare the soil radon concentration contours shown in Figure 1. Figure 2 shows the sub-area median contrast filter processing, and performs grayscale processing on the corresponding sub-area median contrast value filtering results to obtain the sub-area median contrast value filtering results shown in Figure 2. When the window is smaller than 9×9, the effect of the sub-area median contrast filter results is stronger; when the small window increases, the abnormal areas identified by the sub-area median contrast filter generally decrease. When a small window of 3×3 and a large window of 9×9 are used, and the median contrast filter of the sub-area is used to identify 7 positive anomalies in the west and southwest of the measurement area, and when a small window of 2×2 and a large window of 9×9 are used, the 8 positive anomalies in the west and southwest of the measurement area were identified by filtering the median contrast value of the area. According to the above analysis of large and small windows using different parameter ratios, it is found that there is a relationship between the range of positive anomalous signals in the median contrast filter of sub-regions and the distribution of deposits in space.

Next, determine the occurrence probability of the i-th grayscale after grayscale of the corresponding sub-area median-contrast filter image, which is calculated by the following formula: P(i)=N(i)/N; in the formula, P (i) is the probability of the occurrence of the i-th grayscale of the median-contrast filter image of this sub-region after grayscale; N(i) is the i-th grayscale of the sub-region’s median-contrast filtered image after grayscale The total number of occurrences of the degree; N is the total number of pixels after grayscale of the median contrast filter image in this sub-area. When the probability distribution of all pixels after grayscale is the same, the image is the farthest from the focus, the image is the most blurred, and the image entropy value is the largest; otherwise, the image entropy value is the smallest and the image is the clearest ^[19] .

Then, the relationship diagram between large and small window parameters and information entropy value is established through different difference methods. Linear, Cubic, Neareast and V4 interpolation methods are commonly used in Gridata, as shown in Figure 3. , When the ratio of small window parameters is small, the information entropy value is relatively discrete, and there are local differences; as the ratio of large and small window parameters increases, the information entropy deviation is not large; , V4 difference method to do rasterization, to obtain the soil radon concentration grid map shown in Figure 4.

Then build a functional relationship model between the ratio of large and small window parameters and the image information entropy value, as shown in formula (1);

S(m/n)| _min =∫ _m/n (-1)×P(i)×lnP(i) (1);

Calculate the image information entropy when the ratio of the parameters of the large and small windows is m/n, and the probability of the occurrence of the i-th grayscale after the grayscale of the corresponding sub-region median contrast filter image is P(i) by the above functional relationship model According to the relationship between the image information entropy value and the ratio m/n of the large and small window parameters, determine the optimal (when the image information entropy value is the smallest) the ratio of the large and small window parameters, and then determine the large and small window parameter values; Then use the large and small windows corresponding to the aforementioned parameter values to filter the sub-area median contrast value on the soil radon concentration contour map again, and output the final sub-area median contrast value filtering result; The filtering result of the median lining value of the sub-region is used to extract the corresponding abnormal area of soil radon concentration.

Through the analysis of Figure 2 and Figure 5, it is found that the image information entropy value has a low value in the initial segment of the size window parameter ratio in Figure 5, but the information entropy value changes greatly, showing multiple periodic changes, and the stability is relatively low. Difference. However, Figure 2 shows that when the small window is 2×2 and 3×3, and the large window is 6×6, the median contrast filtering result of the sub-area has obvious boundary benefits in the northeast area of the measurement area, that is, the “pseudo-positive abnormal” area. In general, the optimal ratio of large and small window parameters for the information entropy of the measurement area to the median contrast value of the sub-area filter is 15-20.

To sum up, the information entropy value in the measurement area information entropy-sub-area median contrast filter model is 2.5 to 3.6, and the image information entropy value fluctuates periodically with the parameters of large and small windows, and the ratio of the parameters of the large and small windows generally presents " Periodic fluctuation - concave type" polynomial variation law. When the ratio of the size window parameter is small or too large, the information entropy of the filtered image with the median contrast value of the sub-area is large, the reliability of the filtering result of the median-contrast value of the sub-area is reduced, and the median of the optimal area of the measurement area is reduced. The contrast filter size window parameter ratio is 15 to 20, and the image information entropy value is the smallest. Finally, the minimum information entropy-sub-area median contrast filter image results show that the abnormal area in the west of the measurement area is a favorable area for deep uranium resource exploration in this area. .

It should be pointed out that, in the present invention, the method of extracting the abnormal area of soil radon concentration from the filtering result of the median value of the sub-area has no substantial change compared with the prior art. Repeat.

Based on the above-mentioned method for extracting abnormal areas of soil radon concentration, a system for extracting abnormal areas of soil radon concentration based on information entropy-sub-area median contrast value filtering can also be proposed, and the system includes:

The parameter determination unit calculates the parameter ratio of the large and small windows in the median contrast filter of the sub-region as follows, and determines the parameters of the large and small windows according to the calculated parameter ratio:

S(m/n)| _min =∫ _m/n (-1)×P(i)×lnP(i) (1);

In formula (1), m is the parameter value of the large window, n is the parameter value of the small window, m/n is the parameter ratio of the large and small windows, and S(m/n)| _min is the parameter ratio of the large and small windows. is the minimum information entropy value of the median-contrast filter image of the corresponding sub-area when m/n, P(i) is the corresponding sub-area under the condition that the large window parameter value is m and the small window parameter value is n The probability of the occurrence of the i-th grayscale after the grayscale of the bit-contrast filter image.

The image processing unit, according to the large and small window parameters determined by the parameter determination unit, uses the large and small windows corresponding to the parameters to filter the subregion median contrast value of the soil radon concentration contour map and output the subregion. District median contrast filter results.

The abnormal area extraction unit extracts the corresponding abnormal area of soil radon concentration according to the filtering result of the median contrast value of the sub-area output by the image processing unit.

The image processing unit is also used to filter the sub-area median contrast value of the soil radon concentration contour map by using a large window with a parameter value of m and a small window with a parameter value of n, and perform the corresponding sub-area median contrast value filtering processing. The value filtering result is grayscaled.

The parameter determining unit is further configured to determine the probability of the occurrence of the i-th grayscale of the sub-region median contrast value filtered image after grayscale processing of the subregion median contrast value filtering result by the image processing unit.

The parameter determination unit determines the probability of occurrence of the i-th grayscale after the grayscale of the corresponding sub-area median-contrast filter image is as follows:

P(i)=N(i)/N(2);

In formula (2), P(i) is the probability of the occurrence of the i-th grayscale of the median-contrast filter image of the sub-region after graying; N(i) is the gray-scale of the median-contrast filter image of the sub-region. The total number of occurrences of the i-th grayscale after the grayscale; N is the total number of pixels after grayscale of the median-contrast filter image in this sub-region.

Those skilled in the art should understand that the above-mentioned system for extracting abnormal areas of soil radon concentration based on information entropy-sub-area median contrast value filtering can be packaged in a computer software system and stored in a storage device for execution by a computing device , the present invention is not limited to a specific combination of hardware and software.

The invention regards the entire measurement area as an information source providing multiple information, and combines the information entropy with the sub-area median contrast filtering image processing technology to construct a large- and small-window ratio in the sub-area median contrast filtering. The functional relationship model with the image entropy, the parameter ratio of the optimal large and small windows in the median contrast value filtering of the sub-region can be calculated through the functional relationship model, and the subsequent sub-region median contrast value filtering processing can be determined. The method avoids the problem of uncertainty in the data processing results of the median contrast value filtering in the subregion caused by human experience factors, and ensures the accuracy of the identification of abnormal regions by the median contrast value filtering in the subregion. It can provide a valuable reference for delineating the exploration location and mineralization range of uranium deposits.

The above-mentioned embodiment is a preferred implementation scheme of the present invention. In addition, the present invention can also be implemented in other ways, and any obvious replacements are within the protection scope of the present invention without departing from the concept of the technical solution.

In order to make it easier for those skilled in the art to understand the improvements of the present invention relative to the prior art, some drawings and descriptions of the present invention have been simplified, and for the sake of clarity, some other elements are also omitted in this application document, One of ordinary skill in the art would realize that these omitted elements may also constitute the subject matter of the present invention.

Claims (9)

1. The method for extracting the soil radon concentration abnormal region based on the information entropy-median liner value filtering in the sub-region is characterized in that the parameter ratio of a large window to a small window in the median liner value filtering in the sub-region is determined according to the following formula:

S(m/n)|_min＝∫_m/n(-1)×P(i)×lnP(i) (1)；

in the formula (1), m is the parameter value of a large window, n is the parameter value of a small window, m/n is the parameter ratio of the large window and the small window, S (m/n) is_minP (i) is the minimum information entropy value of a median liner value filtering image in a sub-region corresponding to the condition that the parameter ratio of a large window and a small window is m/n, and P (i) is the probability of the i-th level gray level of the corresponding median liner value filtering image in the sub-region after graying under the condition that the parameter value of the large window is m and the parameter value of the small window is n;

determining parameter values of a large window and a small window according to the parameter ratio determined by the formula (1), using the large window and the small window corresponding to the parameter values to conduct filtering processing on the soil radon concentration contour map to obtain a sub-region median liner value, and extracting a corresponding soil radon concentration abnormal region according to the filtering result of the sub-region median liner value.

2. The method for extracting the radon concentration abnormal region in the soil based on the information entropy-median liner value filtering in the subarea according to claim 1, which is characterized in that: the method also comprises the step of measuring the soil radon concentration and forming a soil radon concentration contour map.

3. The method for extracting the radon concentration abnormal region in the soil based on the information entropy-median liner value filtering in the subarea according to claim 1, which is characterized in that: and the method also comprises the steps of performing median lining value filtering processing on the soil radon concentration contour map in the subarea and performing graying processing on the median lining value filtering result in the subarea by adopting a large window with the parameter value of m and a small window with the parameter value of n.

4. The method for extracting radon concentration abnormal regions in soil based on information entropy-median liner value filtering in subareas according to claim 3, which is characterized by comprising the following steps of: after the corresponding sub-area median substrate value filtering result is grayed, the method also comprises the step of determining the occurrence probability of the i-th level gray level after the sub-area median substrate value filtering image is grayed.

5. The method for extracting the radon concentration abnormal region in the soil based on the information entropy-median liner value filtering in the subarea as claimed in claim 4, wherein: determining the i-th level gray degree occurrence probability of the grayed image of the corresponding median liner value filtering image in the subarea according to the following formula:

P(i)＝N(i)/N (2)；

in the formula (2), P (i) is the probability of the i-th level gray level after the median-value filtering image in the subarea is grayed; n (i) is the total number of times of the i-th level gray level after the median lining value filtering image graying in the subarea; and N is the total number of pixels of the grayed median contrast value filtering image in the sub-area.

6. System for extracting soil radon concentration abnormal region based on information entropy-median lining value filtering in subregion, its characterized in that includes:

the parameter determining unit calculates the parameter ratio of the large window and the small window in the median liner value filtering in the subarea according to the following formula (1), and determines the parameters of the large window and the small window according to the calculated parameter ratio:

S(m/n)|_min＝∫_m/n(-1)×P(i)×lnP(i) (1)；

in the formula (1), m is the parameter value of the large window, n is the parameter value of the small window, m/n is the parameter ratio of the large window and the small window, S (m/n) & gtY_minP (i) is the minimum information entropy value of a median liner value filtering image in a corresponding sub-area when the parameter ratio of a large window and a small window is m/n, and P (i) is the probability of the i-th level gray level after the corresponding median liner value filtering image in the sub-area is grayed under the condition that the parameter of the large window is m and the parameter of the small window is n;

the image processing unit is used for making a sub-region median liner value filtering treatment on the soil radon concentration contour map by using the large window and the small window corresponding to the parameters according to the large window parameter and the small window parameter determined by the parameter determination unit and outputting a sub-region median liner value filtering result;

and the abnormal region extraction unit is used for extracting a corresponding soil radon concentration abnormal region from the filtering result of the median lining value in the subarea output by the image processing unit.

7. The system for extracting radon concentration abnormal regions in soil based on the information entropy-median liner value filtering in the subareas as claimed in claim 6, wherein: the image processing unit is also used for carrying out sub-region median lining value filtering processing on the soil radon concentration contour map by adopting a large window with a parameter value of m and a small window with a parameter value of n and carrying out graying processing on corresponding sub-region median lining value filtering results.

8. The system for extracting radon concentration abnormal regions in soil based on the information entropy-median liner value filtering in the subareas as claimed in claim 7, wherein: the parameter determining unit is also used for determining the probability of the i-th gray level after the gray level of the filtered image of the median substrate value in the sub-area is grayed after the image processing unit grays the filtered result of the median substrate value in the corresponding sub-area.

9. The system for extracting radon concentration anomaly region in soil based on information entropy-median liner value filtering in subareas according to claim 8, characterized by: the parameter determining unit determines the i-th level gray degree occurrence probability of the grayed median liner value filtering image in the corresponding sub-area according to the following formula:

in the formula (2), P (i) is the probability of the i-th level gray level after the median-value filtering image in the subarea is grayed; n (i) is the total times of the ith gray level after the median lining value filtering image graying in the subarea appears; and N is the total number of pixels of the grayed median contrast value filtering image in the sub-area.

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