CN115713531B - InSAR-based earth surface image data processing system - Google Patents
InSAR-based earth surface image data processing system Download PDFInfo
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Abstract
The invention relates to the technical field of image data processing, in particular to an InSAR-based earth surface image data processing system. The system comprises: the image data preprocessing module is used for acquiring a difference value elevation chart according to the elevation chart and dividing the elevation chart and the deformation rate chart into areas based on the difference value elevation chart; the image data analysis module is used for acquiring a pillar distribution significant value according to the area range of the category in the elevation map, acquiring a pillar rectangular significant value based on the edge pixel points, and combining the pillar distribution significant value and the pillar rectangular significant value; the mining area collapse detection module is used for acquiring the waveform ground collapse significant value according to the pixel points of the deformation rate diagram and the mining pillar significant value, and determining the ground collapse condition of the monitored mining area based on the waveform ground collapse significant value. The method and the device realize judgment of the collapse condition of the wavy ground, and improve the accuracy of data processing of the monitored mining area based on the InSAR earth surface image.
Description
Technical Field
The invention relates to the technical field of image data processing, in particular to an InSAR-based earth surface image data processing system.
Background
In the mining process of the mining area, due to improper mining operation and other reasons, the original stress of rock mass around the mining area of the mining area is easy to damage, and when the damage reaches a certain degree, obvious continuous change, deformation and discontinuous damage of rock stratum and ground surface occur, namely the mining area ground surface is collapsed, so that the ground surface of the mining area needs to be monitored.
The mining area ground subsidence can bring great harm to agricultural production and ecological environment around the mining area, personnel casualties and damage to infrastructure can be caused, in order to reduce personnel casualties and prevent harm caused by mining area subsidence, the mining area ground subsidence condition needs to be identified, the existing method carries out laser radar scanning on the mining area to be measured, cloud data of the laser radar of the mining area are obtained, the cloud data of the laser radar are filtered twice, then the selected reference area and the cloud data of the reference area are derived, point set groups of the reference area are obtained, the point set groups are divided into a plurality of point sets, a difference grid diagram of each point set group is obtained by utilizing a natural neighborhood interpolation tool, the difference grid diagram of the mining area to be measured is obtained according to the difference grid diagrams, the mining area coal mining basin is measured through the difference grid diagram, but the method is to obtain the difference grid diagram of the whole mining area to be measured, the mining area to be measured has different areas of different structures such as mining pillars, mining rooms and roadways, the mining area subsidence information of different areas can be accurately obtained, and the mining area subsidence data of the mining area can be accurately processed if the mining area surface subsidence information of the mining area is obtained, and the mining area subsidence data of the mining area is accurately obtained.
Disclosure of Invention
In order to solve the problem of inaccurate mining area collapse data, the invention aims to provide an InSAR-based earth surface image data processing system, and the adopted technical scheme is as follows:
the invention provides an InSAR-based earth surface image data processing system, which comprises:
the image data preprocessing module is used for acquiring an elevation map and a deformation rate map of the monitored mining area; obtaining a difference value elevation graph according to pixel values of pixel points in the elevation graph; dividing pixel points in the difference elevation map into pillar pixel points and collapse pixel points through a segmentation threshold; acquiring a pillar subarea of an elevation chart and a pillar area and a collapse area in a deformation rate chart according to the pillar pixel points and the collapse pixel points;
the image data analysis module is used for dividing the pillar subareas in the elevation map into at least two categories and acquiring the area range of the pillar subareas in each category; obtaining a pillar distribution significant value by combining the number of pillar subareas and the area range of each category; performing linear detection on each pillar sub-region to obtain at least two standard lines, and acquiring a pillar rectangular significant value based on edge pixel points of each pillar sub-region and the target lines; combining the pillar distribution significance value with the pillar rectangular significance value;
The mining area collapse detection module is used for respectively clustering pixel points in the mining pillar area and the collapse area in the deformation rate diagram to correspondingly obtain mining pillar clusters and collapse clusters; combining the area of the pillar area, the area of the subsidence area, the pillar clusters, the subsidence clusters and the pillar significant values to obtain wavy ground subsidence significant values; determining a ground collapse condition of the monitored mine area based on the wavy ground collapse significance value.
Further, the obtaining the difference elevation map according to the pixel value of the pixel point in the elevation map in the image data preprocessing module includes:
and selecting the minimum value of the pixel values of all the pixel points in the elevation chart as the minimum pixel value, calculating the difference value between the pixel value of each pixel point in the elevation chart and the minimum pixel value as the pixel difference value of the corresponding pixel point, and respectively taking the pixel difference value of each pixel point in the elevation chart as the pixel value of the corresponding pixel point in the difference elevation chart to obtain the difference elevation chart.
Further, the obtaining, in the image data preprocessing module, the pillar sub-region of the elevation map and the pillar region and the collapse region in the deformation rate map according to the pillar pixel points and the collapse pixel points includes:
acquiring pixel points of corresponding positions of each pillar pixel point of the difference value elevation chart in the elevation chart as first pixel points, and taking a region formed by the first pixel points as a pillar sub-region of the elevation chart; respectively acquiring pixel points of corresponding positions of pillar pixels and collapse pixels of a difference elevation chart in a deformation rate chart as a second pixel point and a third pixel point, taking a region formed by the second pixel point as a pillar subarea of the deformation rate chart, taking a region formed by all pillar subareas in the deformation rate chart as a pillar region in the deformation rate chart, and taking a region formed by the third pixel point as a collapse region of the deformation rate chart.
Further, the dividing the pillar sub-regions in the elevation map into at least two categories in the image data analysis module includes:
counting the number of pixel points in each pillar sub-region in the elevation chart to be used as the area of the corresponding pillar sub-region, taking the area of the pillar sub-region as the numerical value of each pixel point in the corresponding pillar sub-region, clustering all the pixel points in all the pillar sub-regions according to the numerical value of the pixel points to obtain at least two categories, taking any one category as a reference category, dividing the corresponding pillar sub-region into the reference category when the reference category contains all the pixel points in any one pillar sub-region, and taking the corresponding pillar sub-region as an isolated pillar sub-region when the reference category contains part of the pixel points in any one pillar sub-region.
Further, the obtaining the pillar distribution saliency value by combining the number of pillar subareas and the area margin of each category in the image data analysis module comprises:
counting the number of isolated pillar sub-areas as a first number, calculating the sum of the area range of pillar areas in all categories as a range sum, counting the number of categories, and taking the number of categories as a first numerator, the product of the first number and the range sum as a first denominator to obtain a first ratio as a pillar distribution significance value.
Further, the obtaining, in the image data analysis module, a pillar rectangular saliency value based on the edge pixel point of each pillar sub-region and the target straight line includes:
taking any one pillar sub-region as an example pillar sub-region, taking the minimum circumscribed rectangle of the example pillar sub-region, respectively selecting edge pixel points closest to four corners of the minimum circumscribed rectangle from edge pixel points of the example pillar sub-region as target edge pixel points, taking the edge pixel points between two adjacent target edge pixel points as a set, and respectively carrying out straight line fitting on the edge pixel points in each set to obtain the fitting goodness of the corresponding set in the example pillar sub-region;
setting a linear coefficient and a constant coefficient, counting the number of target lines in an example pillar subarea as a second number, taking the absolute value of the difference between the second number and the linear coefficient as a number difference, taking the sum of the fitting goodness of each set in the example pillar subarea as a second numerator, taking the sum of the number difference and the constant coefficient as a second denominator to obtain a second ratio as a first result of the example pillar subarea, and taking the sum of the first results of all the pillar subareas as a pillar rectangular significant value.
Further, combining the pillar distribution saliency value with the pillar rectangular saliency value in the image data analysis module includes:
taking the product of the pillar distribution significance value and the pillar rectangle significance value as the pillar significance value.
Further, the acquiring the wavy ground subsidence significance value by combining the area of the pillar region, the area of the subsidence region, the pillar clusters, the subsidence clusters and the pillar significance value in the mining area subsidence detection module comprises:
taking the number of pixel points in a pillar region in a deformation rate diagram as the area of the pillar region and the number of pixel points in a collapse region as the area of the collapse region, counting the number of pillar clusters in the deformation rate diagram as the third number and the number of collapse clusters as the fourth number, taking the area of the collapse region as a third numerator and the area of the pillar region as a third denominator to obtain a third ratio, taking the fourth number as a fourth numerator and the third number as a fourth denominator to obtain a fourth ratio, taking the product of the third ratio and the fourth ratio as a second result, taking the logarithm of the pillar significant value taking a preset value as a base as a third result, and taking the product of the second result and the third result as the wavy ground collapse significant value.
Further, the determining a ground collapse condition for monitoring the mine area based on the wavy ground collapse significance value comprises: setting a collapse threshold and a difference threshold, when the collapse significant value of the wavy ground is larger than or equal to the collapse threshold, monitoring that the wavy ground collapses in the mining area, when the collapse significant value of the wavy ground is smaller than the collapse threshold, respectively calculating the average value of the pixel values of all the pixel points in the mining pillar area in the elevation map as the pixel average value of the mining pillar area, and the average value of the pixel values of all the pixel points in the collapse area as the pixel average value of the collapse area, and when the absolute value of the difference value between the pixel average value of the mining pillar area and the pixel average value of the collapse area is larger than or equal to the difference threshold, monitoring that the ground collapses in the mining area.
Further, the dividing the pixels in the difference elevation map into pillar pixels and collapse pixels by the segmentation threshold in the image data preprocessing module includes:
and obtaining a segmentation threshold value by using a maximum inter-class variance method for pixel values of the pixel points in the difference elevation map, classifying the pixel points with the pixel values larger than the segmentation threshold value in the difference elevation map as pillar pixel points, and classifying the pixel points with the pixel values smaller than or equal to the segmentation threshold value in the difference elevation map as collapse pixel points.
The invention has the following beneficial effects:
the altitude of the position where the monitored mining area is located is different, so that the range difference of the pixel values corresponding to each pixel point on the elevation map corresponding to the monitored mining area is possibly larger, the analysis is not facilitated, and a difference elevation map is obtained according to the pixel values of the pixel points in the elevation map; the method comprises the steps of dividing different structural areas of a monitored mining area into mining pillar pixel points and collapse pixel points in a difference elevation chart according to a segmentation threshold value, and acquiring mining pillar subareas of the elevation chart and mining pillar areas and collapse areas in a deformation rate chart according to the mining pillar pixel points and the collapse pixel points, wherein the mining pillar areas and the collapse areas in the deformation rate chart are obtained; the underground ore pillars of the monitored mining area play a supporting role for protecting personnel safety and the integrity of the roadway, and the ore pillars of the monitored mining area have the following characteristics: the ore pillar rule is distributed over the whole monitoring ore area, the ore pillar in the monitoring ore area is generally square or rectangular, the ore pillar position characteristic and the rectangular morphological characteristic which are still supported and displayed at the position of the ore pillar after collapse can be obtained according to the two characteristics, the characteristic of the ore pillar is distributed over the whole monitoring ore area according to the ore pillar rule, the ore pillar distribution significant value which can represent the ore pillar position characteristic is obtained by combining the number of the ore pillar subareas and the area range of each category, the ore pillar rectangular significant value which can represent the ore pillar rectangular morphological characteristic is obtained according to the ore pillar in the monitoring ore area is generally square or rectangular on the basis of the edge pixel point and the target straight line of each ore pillar subarea, the ore pillar significant value reflects the significant degree of the ore pillar characteristic which is represented by the difference of the ore pillar area and the collapse area which are displayed on the ground of the ore area, and the ore pillar distribution significant value and the ore pillar rectangular significant value are combined to obtain the ore pillar significant value; the pixel values corresponding to the pixel points in the deformation rate diagram are the displacement speed of the earth surface corresponding to the deformation rate subsidence area of the position, and as the characteristic shape of the wavy ground subsidence basin is obvious, the ground deformation is insufficient, the ground deformation is larger according to the subsidence area, and the ground deformation of the ore pillar area is smaller, so that the pixel points in the ore pillar area and the subsidence area in the deformation rate diagram are clustered respectively, ore pillar clusters and subsidence clusters are correspondingly obtained, the area of the ore pillar area, the area of the subsidence area, the ore pillar clusters, the subsidence clusters and the ore pillar significant values are combined to obtain the wavy ground subsidence significant value, and the ground subsidence condition of the monitored ore area is obtained according to the wavy ground subsidence significant value of the monitored ore area, so as to evaluate whether the wavy ground subsidence occurs on the ground of the monitored ore area; by respectively analyzing the pillar area and the subsidence area of different structural areas of the monitored mining area, the analysis of the subsidence condition of the monitored mining area is more accurate, and the situation of errors is smaller, so that the accuracy of detecting the subsidence of the ground of the mining area is improved by performing data processing on the earth surface image of the monitored mining area.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of an InSAR-based earth surface image data processing system in accordance with one embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of an InSAR-based earth surface image data processing system according to the present invention with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention aims at the specific scene: and acquiring surface related data of the mining area by using InSAR, and identifying the wave-shaped ground subsidence of the surface of the mining area.
The following specifically describes a specific scheme of an InSAR-based earth surface image data processing system provided by the invention with reference to the accompanying drawings.
Referring now to FIG. 1, shown is a block diagram of an InSAR-based earth surface image data processing system in accordance with one embodiment of the present subject matter, the system comprising: the mining area collapse detection system comprises an image data preprocessing module, an image data analysis module and a mining area collapse detection module.
The image data preprocessing module 01 is used for acquiring an elevation map and a deformation rate map of the monitored mining area; obtaining a difference value elevation graph according to pixel values of pixel points in the elevation graph; dividing pixel points in the difference elevation map into pillar pixel points and collapse pixel points through a segmentation threshold; and acquiring a pillar subarea of the elevation chart and a pillar area and a collapse area in the deformation rate chart according to the pillar pixel points and the collapse pixel points.
In order to obtain surface deformation information with relatively high precision, a DINSAR or time sequence InSAR technology is utilized to obtain multi-period SAR data of the same area covered by the surface deformation information, SAR data of a monitored mining area is queried according to a AlaskaSatelliteFacility (ASF) data query website, and surface deformation rate and elevation information of the monitored mining area are extracted. The surface deformation rate and the elevation information of the monitored mining area respectively form a deformation rate diagram and an elevation diagram of the monitored mining area, the pixel value corresponding to each pixel point on the deformation rate diagram is the deformation rate corresponding to the position, and the pixel value corresponding to each pixel point on the elevation diagram is the height corresponding to the position. The process of extracting and monitoring the surface deformation rate and elevation information of the mining area is a well-known technology, and a specific method is not described herein.
Because the elevation of the position where the monitored mining area is located has a difference, the range difference of pixel values corresponding to each point on the elevation map corresponding to the monitored mining area may be larger, and the analysis of the information of the monitored mining area is not facilitated, so that a difference elevation map is acquired according to the elevation map, and the method for acquiring the difference elevation map is as follows: and selecting the minimum value of the pixel values of all the pixel points in the elevation chart as the minimum pixel value, calculating the difference value between the pixel value of each pixel point in the elevation chart and the minimum pixel value as the pixel difference value of the corresponding pixel point, and respectively taking the pixel difference value of each pixel point in the elevation chart as the pixel value of the corresponding pixel point in the difference elevation chart to obtain the difference elevation chart.
To facilitate identification of the wavy ground subsidence of the surface of the monitored mine, different ground conditions of the monitored mine are partitioned based on each pixel point of the difference elevation map. The wave-shaped ground subsidence mainly refers to the phenomenon that the overlying rock soil body on the goaf is cut and raised, collapsed or sunk due to the fact that ore pillars are crushed, collapsed or slipped in a chain manner in a room pillar method exploitation area, so that the wave-shaped ground subsidence phenomenon is formed on the ground. When the wavy ground subsidence occurs on the ground of the monitored mining area, the subsidence amplitude of the ground of the mining area such as a mining room and a roadway is obviously larger than that of the mining pillar distribution area, so that the mining area such as the mining pillar, the mining room and the roadway can be divided at the corresponding positions of the ground of the monitored mining area according to the obvious difference of the subsidence amplitude.
According to the underground structure of the monitored mining area, respectively acquiring a mining pillar subarea, a mining pillar area and a subsidence area of the elevation map and the deformation rate map, and carrying out area division on the elevation map and the deformation rate map by the following steps:
and obtaining a segmentation threshold value by using a maximum inter-class variance method for pixel values of the pixel points in the difference elevation map, classifying the pixel points with the pixel values larger than the segmentation threshold value in the difference elevation map as pillar pixel points, and classifying the pixel points with the pixel values smaller than or equal to the segmentation threshold value in the difference elevation map as collapse pixel points. Acquiring pixel points of corresponding positions of each pillar pixel point of the difference value elevation chart in the elevation chart as first pixel points, and taking a region formed by the first pixel points as a pillar sub-region of the elevation chart; respectively acquiring pixel points of corresponding positions of pillar pixels and collapse pixels of a difference elevation chart in a deformation rate chart as a second pixel point and a third pixel point, taking a region formed by the second pixel point as a pillar subarea of the deformation rate chart, taking a region formed by all pillar subareas in the deformation rate chart as a pillar region in the deformation rate chart, and taking a region formed by the third pixel point as a collapse region of the deformation rate chart.
As an example, a segmentation threshold value of a maximum inter-class variance method is obtained based on pixel values of all pixel points in the difference elevation map, and the pixel points with the pixel values larger than the segmentation threshold value in the difference elevation map are classified as pillar pixel points and the pixel points with the pixel values smaller than or equal to the segmentation threshold value are classified as collapse pixel points. Acquiring pixel points of corresponding positions of all ore pillar pixel points of a difference elevation chart in the elevation chart, taking an area formed by the pixel points as an ore pillar subarea, taking an area formed by all the ore pillar subareas as an ore pillar area, taking pixel points of corresponding positions of all collapse pixel points of the difference elevation chart in the elevation chart, and taking an area formed by the pixel points as a collapse area, thereby acquiring the ore pillar subarea, the ore pillar area and the collapse area in the elevation chart; the pillar sub-region, pillar region and collapse region of the deformation rate map are obtained using the same acquisition method as the pillar sub-region, pillar region and collapse region in the elevation map.
The image data analysis module 02 is used for dividing the pillar subareas in the elevation map into at least two categories, and acquiring the area range of the pillar subareas in each category; obtaining a pillar distribution significant value by combining the number of pillar subareas and the area range of each category; performing linear detection on each pillar sub-region to obtain at least two standard lines, and acquiring a pillar rectangular significant value based on edge pixel points of each pillar sub-region and the target lines; and combining the pillar distribution significance value with the pillar rectangular significance value.
The underground ore pillars of the monitored mining area play a supporting role for protecting personnel safety and the integrity of the roadway, the ore pillars are generally square or rectangular and regularly arranged, so that the stress is balanced, the ore pillars are generally distributed over the whole monitored mining area, and the position distribution characteristics and the rectangular morphological characteristics of the ore pillars can be obtained according to the characteristics.
The pillars are arranged to maintain the stability of the formation so that the size of each pillar is uniform. Although the actual limitation to arranging the ore pillars in different areas is different, each ore pillar in the whole monitoring mining area cannot be kept consistent, the size of the ore pillars with similar distances is basically consistent, and the ore pillar subareas corresponding to the ore pillars can be divided into one category. When the wavy ground subsides, the number of the pillar subareas which cannot be clustered into any one category is small, the number of the pillar subareas which can be clustered into a certain category is large, and the areas of the pillar subareas in any one category are consistent, so that the area difference of the pillar subareas in any one category is small, and the position distribution characteristics of the pillars are obtained by combining the area difference of the pillar subareas in each category.
In order to obtain the position distribution characteristics of the ore pillars, the ore pillar subareas corresponding to each ore pillar are required to be classified according to the area of the ore pillar, and isolated ore pillar subareas which cannot be classified into any one of the classifications are required to be screened out so as to analyze the ore pillar subareas of different classifications. The division method of the ore pillar subareas comprises the following steps: counting the number of pixel points in each pillar sub-region in the elevation chart to be used as the area of the corresponding pillar sub-region, taking the area of the pillar sub-region as the numerical value of each pixel point in the corresponding pillar sub-region, clustering all the pixel points in all the pillar sub-regions according to the numerical value of the pixel points to obtain at least two categories, taking any one category as a reference category, dividing the corresponding pillar sub-region into the reference category when the reference category contains all the pixel points in any one pillar sub-region, and taking the corresponding pillar sub-region as an isolated pillar sub-region when the reference category contains part of the pixel points in any one pillar sub-region.
As an example, the number of all pillar sub-regions in the elevation chart is counted as m1, the number of pixel points in each pillar sub-region is counted, the number of pixel points in the pillar sub-region is used as the area of the corresponding pillar sub-region, that is, the values corresponding to the pixel points in the same pillar sub-region are the same, so as to be E 1 Is a neighborhood radius threshold, MP 1 And clustering each pixel point in all the pillar subareas in the elevation chart by using a DBSCAN algorithm according to the numerical value of the pixel point in the pillar subareas to obtain each category. Because the values of all the pixel points in one pillar subarea are the same, all the pixel points in the pillar subarea can be clustered into the same category; when some pixel points in one pillar subarea cannot be clustered to any one category, the fact that all the pixel points in the pillar subarea cannot be clustered to any one category is indicated, the pillar subarea is taken as an isolated pillar subarea at the moment, and the number of the isolated pillar subareas is counted as m2.
Preferably, in the embodiment of the invention, the neighborhood radius threshold takes an empirical value of 100, and the neighborhood density threshold MP 1 Take the empirical value 20.
It should be noted that, the method for selecting DBSCAN to cluster each pixel point in all pillar subareas in the invention is not described herein, and is a technical means known to those skilled in the art.
Selecting the maximum value and the minimum value of the area of the pillar subarea from each category respectively, taking the difference value between the maximum value of the area of the pillar subarea and the minimum value of the area of the pillar subarea in one category as the area range of the pillar area in the corresponding category, sequentially obtaining the area range of the pillar area in each category, and respectively marking as e 1 ，e 2 ，...，e m3 Wherein e is 1 The area of the pillar region is extremely poor in category 1, e 2 The area of the pillar region is extremely poor, e, for category 2 middling m3 The area of the pillar area was very poor for the m3 th category. Counting the number of isolated pillar sub-regions as a first number, and calculating the area limit of pillar regions in all categoriesCounting the number of categories by taking the sum of the categories as the range sum and taking a first ratio obtained by taking the number of the categories as a first numerator and taking the product of the first number and the range sum as a first denominator as a pillar distribution significance value, wherein the calculation formula of the pillar distribution significance value is as follows:
wherein m2 is the number of isolated pillar sub-regions, m3 is the number of classes obtained by clustering pillar sub-regions in the elevation map, e x The area of the pillar area is very poor for the x-th category.
It should be noted that, the division of the pillar regions in the elevation map is essentially based on the areas of pillar subregions, when the number of the isolated pillar subregions is larger, the larger the difference between the areas of the isolated pillar subregions and the areas of other pillar subregions in the monitored mining area is, the uneven distribution arrangement of the pillars in the monitored mining area is caused, and the smaller the distribution significance of the pillars is; when the average value of the area extremely poor of the pillar regions in the category is smaller, the area of each pillar region in all the categories is basically consistent, namely the area of each pillar region in the elevation chart is basically the same, which indicates that the pillars in the monitored mining area are easier to be uniformly arranged, so that the distribution significance of the pillars is smaller.
The shape of the ore pillar is square or rectangular, so that the ore pillar can better support a monitored ore area, the safety of protecting personnel and the integrity of a roadway can be greatly improved, and the rectangular significant value of the ore pillar is obtained by judging whether each side of the ore pillar meets the characteristics of the side of the rectangle or not and obtaining the number of straight lines in the area of the ore pillar.
Judging whether each side of the pillar subarea accords with the characteristics of each side of the rectangle or not through the fitting goodness, and further judging the pillar rectangle significant value capable of presenting the rectangular morphological characteristics. The method for obtaining the fitting goodness of the pillar subregion comprises the following steps: taking any one pillar sub-region as an example pillar sub-region, taking the minimum circumscribed rectangle of the example pillar sub-region, respectively selecting edge pixel points closest to four corners of the minimum circumscribed rectangle from edge pixel points of the example pillar sub-region as target edge pixel points, taking the edge pixel points between two adjacent target edge pixel points as a set, and respectively carrying out straight line fitting on the edge pixel points in each set to obtain the fitting goodness of the corresponding set in the example pillar sub-region.
As an example, any one pillar sub-region is taken as an example pillar sub-region, the smallest circumscribing rectangle of the example pillar sub-region is taken, four edge pixel points closest to four corners of the smallest circumscribing rectangle are respectively selected from edge pixel points of the example pillar sub-region, and the four edge pixel points are taken as target edge pixel points. Taking the edge pixel point between two adjacent target edge pixel points as one set, the number of sets in the example pillar sub-area is f, and it should be noted that, in the embodiment of the present invention, the number f of sets in the example pillar sub-area takes a test value of 4, that is, all the edge pixel points in the example pillar sub-area can be divided into four sets based on the target edge pixel points, that is, each set corresponds to each side of the example pillar sub-area. Performing straight line fitting on edge pixel points contained in each set of the sample pillar sub-region by using a least square method, wherein each set respectively obtains a fitting goodness, and the sample pillar sub-region has four fitting goodness which are respectively marked as r i，1 ，r i，2 ，r i，3 ，r i，4 Wherein r is i，1 The goodness of fit, r, for the 1 st set of ith pillar subregions i，2 Goodness of fit, r, for the 2 nd set of ith pillar subregions i，3 Goodness of fit, r, for the 3 rd set of ith pillar subregions i，4 The ith pillar subregion 4 Goodness of fit for the individual sets; and respectively acquiring all the goodness of fit of each pillar subarea in the elevation map.
It should be noted that, the method of the present invention selects the least square method to perform straight line fitting on the edge pixel points, and the specific method is not described herein, which is a technical means known to those skilled in the art.
Simultaneously, carrying out Hough straight line detection on each pillar subarea in the elevation map to obtainThe number of the target straight lines in any pillar subarea is h i . Obtaining a pillar rectangular saliency value by combining the number of pillar regions in the elevation map, the target straight line in each pillar region and the fitting goodness of the set in each pillar region: setting a linear coefficient and a constant coefficient, counting the number of target lines in an example pillar subarea as a second number, taking the absolute value of the difference between the second number and the linear coefficient as a number difference, taking the sum of the fitting goodness of each set in the example pillar subarea as a second numerator, taking the sum of the number difference and the constant coefficient as a second denominator to obtain a second ratio as a first result of the example pillar subarea, and taking the sum of the first results of all the pillar subareas as a pillar rectangular significant value.
The calculation formula of the pillar rectangle salient value v is as follows:
wherein m1 is the number of all pillar sub-regions in the elevation map, r i，j A fitting goodness, h for the j-th set in the j-th pillar sub-region i Taking a tested value of 4 for the number of target straight lines in the first pillar subarea, wherein f is the number of sets in each pillar subarea; a1 is a straight line coefficient, takes an empirical value of 4, a2 is a constant coefficient, and acts as a denominator of 0, and takes an empirical value of 1; i is an absolute function.
It should be noted that, when the number of the target straight lines in each pillar sub-area is closer to 4, it indicates that the shape of the pillar corresponding to the pillar sub-area is closer to a rectangle or rectangle, so that the rectangular feature of the pillar is more obvious, the rectangular significant value of the pillar is greater; the goodness of fit indicates the degree of fit of the straight line and each edge pixel point in the set, and as the edge pixel points in the set can represent the edges of the ore pillar, the larger the goodness of fit of each set in the ore pillar sub-area is, the closer the distribution of the edge pixel points in the set is to the straight line, namely, the straighter each edge of the ore pillar is, the more accords with the characteristics of the rectangle, and the larger the salient value of the rectangle of the ore pillar is.
According to the ore pillar distribution significant value representing the position distribution characteristic and the ore pillar rectangular significant value representing the rectangular morphological characteristic, obtaining an ore pillar significant value, taking the product of the ore pillar distribution significant value and the ore pillar rectangular significant value as the ore pillar significant value, and calculating the ore pillar significant value as follows:
c＝p*v
Wherein p is a pillar distribution significant value, and v is a pillar rectangular significant value.
It should be noted that the remarkable value of the ore pillar reflects the remarkable degree of the characteristic of the ore pillar presented on the ground of the monitored ore area, the underground ore pillar of the ore area plays a supporting role for protecting personnel safety and tunnel integrity, when the ore pillar is square or rectangular and is regularly arranged, the monitored ore area can be better supported, when the remarkable value of the ore pillar distribution is larger, the arrangement of the ore pillar in the monitored ore area is more regular, when the remarkable value of the ore pillar rectangular is larger, the shape of the ore pillar is close to the rectangular, the ore pillar plays a better supporting role on the monitored ore pillar, and the remarkable value of the ore pillar is larger.
The mining area collapse detection module 03 is used for respectively clustering pixel points in the mining pillar area and the collapse area in the deformation rate diagram to correspondingly obtain mining pillar clusters and collapse clusters; combining the area of the pillar area, the area of the subsidence area, the pillar clusters, the subsidence clusters and the pillar significant values to obtain wavy ground subsidence significant values; determining a ground collapse condition of the monitored mine area based on the wavy ground collapse significance value.
The subsidence basin on the wavy ground has larger area, the wave crest part corresponds to the underground ore pillar position, the wave trough part corresponds to the underground ore room and the roadway position, the basin has obvious morphological characteristics, the ground deformation is insufficient, namely the ground inclination, the curvature and the horizontal deformation are large, the ground in the subsidence area is wavy, and the relative height difference between the wave crest and the wave trough is 3-10 m.
The number of pixel points in the pillar region in the deformation rate diagram is taken as the area s1 of the pillar region, and the number of pixel points in the collapse region is taken as the area s2 of the collapse region, and the area s2 of the collapse region is larger than the area s1 of the pillar region due to the larger area of the collapse basin on the wavy ground.
Each pixel point in the deformation rate diagramThe corresponding pixel value is the displacement speed of the surface corresponding to the deformation rate subsidence area at the position, and the characteristic form of the wavy ground subsidence basin is obvious and the ground deformation is insufficient, so that the difference of the pixel values corresponding to the pixel points in the subsidence area is large, the pixel values of the pixel points in the pillar area are basically unchanged, and the pixel points in the pillar area and the subsidence area are respectively divided for the convenience of analysis. With MP 2 For the neighborhood density threshold value, clustering the pixel points in a pillar region and a collapse region in a deformation rate diagram according to the pixel values of the pixel points by using an OPTICS algorithm, taking a cluster obtained by clustering the pixel points in the pillar region as a pillar cluster, taking a cluster obtained by clustering the pixel points in the collapse region as a collapse cluster, and counting the number of the pillar clusters as n 1 The number of collapsed clusters is n 2 . Because the pixel values corresponding to the pixel points in the collapse area are large in difference, the pixel values of the pixel points in the pillar area are basically unchanged, so that the number n of the collapse clusters is n 2 For the number n of pillar clusters 1 More.
Preferably, in the embodiment of the invention, the neighborhood density threshold takes an empirical value of 5.
In the invention, the OPTICS algorithm is selected to cluster the pixels in the pillar region and the collapse region in the deformation rate diagram, and the specific method is not described herein, which is a technical means known to those skilled in the art.
Based on the area of the pillar area, the area of the subsidence area, the number of pillar clusters and the number of subsidence clusters, and combining the pillar significant values, constructing a wavy ground subsidence significant value reflecting the ground subsidence degree: taking the number of pixel points in a pillar region in a deformation rate diagram as the area of the pillar region and the number of pixel points in a collapse region as the area of the collapse region, counting the number of pillar clusters in the deformation rate diagram as the third number and the number of collapse clusters as the fourth number, taking the area of the collapse region as a third numerator and the area of the pillar region as a third denominator to obtain a third ratio, taking the fourth number as a fourth numerator and the third number as a fourth denominator to obtain a fourth ratio, taking the product of the third ratio and the fourth ratio as a second result, taking the logarithm of the pillar significant value taking a preset value as a base as a third result, and taking the product of the second result and the third result as the wavy ground collapse significant value.
The calculation formula of the significant value w of the wave-like ground collapse is as follows:
wherein s1 is the area of the pillar region, s2 is the area of the collapse region, n 1 To collapse the number of clusters, n 2 C is a significant value of ore pillar; lg is a logarithmic function based on 10.
It should be noted that, when the wavy ground subsides, the area of the subsidence of the ground is larger, and the area of the pillar support which is basically free from subsidence is smaller, so when the area of the subsidence area is larger and the area of the pillar area is smaller, the more likely the wavy ground subsides appear on the ground, namely, the more significant the subsidence value of the wavy ground is; the characteristic form of the wavy ground subsidence basin is obvious, the ground deformation is insufficient, so the difference of pixel values among pixel points in the subsidence area is larger, the pixel values in the ore pillar area and among the pixel points are basically kept unchanged due to the existence of the support of the ore pillar in the ore pillar area, the number of subsidence clusters is more, the number of the ore pillar clusters is basically unchanged, and the wavy ground subsidence significant value is larger; the obvious value of the ore pillar can present the position distribution characteristic and the rectangular morphological characteristic of the ore pillar in the monitored ore area, and when the position distribution characteristic and the rectangular morphological characteristic of the ore pillar are more obvious, the stronger the supporting capacity of the ore pillar on the monitored ore area is indicated, and the wave-shaped ground collapse obvious value of the monitored ore area can be presented.
The method for evaluating and monitoring the collapse condition of the wavy ground in the mining area based on the significant value of the collapse of the wavy ground comprises the following specific steps: setting a collapse threshold and a difference threshold, when the collapse significant value of the wavy ground is larger than or equal to the collapse threshold, monitoring that the wavy ground collapses in the mining area, when the collapse significant value of the wavy ground is smaller than the collapse threshold, respectively calculating the average value of the pixel values of all the pixel points in the mining pillar area in the elevation map as the pixel average value of the mining pillar area, and the average value of the pixel values of all the pixel points in the collapse area as the pixel average value of the collapse area, and when the absolute value of the difference value between the pixel average value of the mining pillar area and the pixel average value of the collapse area is larger than or equal to the difference threshold, monitoring that the ground collapses in the mining area.
As an example, setting a collapse threshold t1 and a difference threshold t2, and when a significant value of the collapse of the wavy ground, which is obtained by carrying out visualization processing on data acquired by the surface corresponding to the monitored mining area, is greater than or equal to the collapse threshold t1, considering that the collapse of the wavy ground occurs in the monitored mining area and outputting the conclusion; when the significant value of the wavy floor collapse is less than the collapse threshold t1, then the monitoring mine is considered to have no wavy floor collapse. When the monitored mining area is judged to be free of wavy ground subsidence, further analysis is needed to be carried out on information of the monitored mining area, at the moment, average values of pixel values of all pixel points in the mining pillar area and the subsidence area in an elevation chart are calculated respectively, the pixel average value of the mining pillar area and the pixel average value of the subsidence area are obtained in sequence, and when the absolute value of the difference value between the pixel average value of the mining pillar area and the pixel average value of the subsidence area is larger than or equal to a difference threshold t2, the monitored mining area is considered to be subsided; when the absolute value of the difference value between the pixel mean value of the mining pillar area and the pixel mean value of the collapse area is smaller than the difference threshold t2, the mining area is considered to be monitored not to collapse, and the conclusion is output.
Preferably, in the embodiment of the invention, the collapse threshold t1 takes an experience value of 2.5, and the difference threshold t2 takes an experience value of 1.5.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. An InSAR-based earth surface image data processing system, comprising:
the image data preprocessing module is used for acquiring an elevation map and a deformation rate map of the monitored mining area; obtaining a difference value elevation graph according to pixel values of pixel points in the elevation graph; dividing pixel points in the difference elevation map into pillar pixel points and collapse pixel points through a segmentation threshold; acquiring a pillar subarea of an elevation chart and a pillar area and a collapse area in a deformation rate chart according to the pillar pixel points and the collapse pixel points;
The image data analysis module is used for dividing the pillar subareas in the elevation map into at least two categories and acquiring the area range of the pillar subareas in each category; obtaining a pillar distribution significant value by combining the number of pillar subareas and the area range of each category; performing linear detection on each pillar sub-region to obtain at least two standard lines, and acquiring a pillar rectangular significant value based on edge pixel points of each pillar sub-region and the target lines; combining the pillar distribution significance value with the pillar rectangular significance value;
the mining area collapse detection module is used for respectively clustering pixel points in the mining pillar area and the collapse area in the deformation rate diagram to correspondingly obtain mining pillar clusters and collapse clusters; combining the area of the pillar area, the area of the subsidence area, the pillar clusters, the subsidence clusters and the pillar significant values to obtain wavy ground subsidence significant values; determining a ground collapse condition of the monitored mining area based on the wavy ground collapse significance value;
the step of obtaining the difference elevation map according to the pixel values of the pixel points in the elevation map in the image data preprocessing module comprises the following steps:
selecting the minimum value of the pixel values of all the pixel points in the elevation chart as the minimum pixel value, calculating the difference value between the pixel value of each pixel point in the elevation chart and the minimum pixel value as the pixel difference value of the corresponding pixel point, and respectively taking the pixel difference value of each pixel point in the elevation chart as the pixel value of the corresponding pixel point in the difference elevation chart to obtain a difference elevation chart;
The obtaining of the pillar distribution significance value by combining the number of pillar subareas and the area range of each category in the image data analysis module comprises the following steps:
counting the number of isolated pillar sub-areas as a first number, calculating the sum of the area range of pillar areas in all categories as a range sum, counting the number of categories, and taking the number of categories as a first numerator, the product of the first number and the range sum as a first denominator to obtain a first ratio as a pillar distribution significance value;
dividing the pillar sub-regions in the elevation map into at least two categories in the image data analysis module includes:
counting the number of pixel points in each pillar sub-region in an elevation chart to be used as the area of the corresponding pillar sub-region, taking the area of the pillar sub-region as the numerical value of each pixel point in the corresponding pillar sub-region, clustering all pixel points in all pillar sub-regions according to the numerical value of the pixel points to obtain at least two categories, taking any one category as a reference category, dividing the corresponding pillar sub-region into the reference category when the reference category contains all pixel points in any one pillar sub-region, and taking the corresponding pillar sub-region as an isolated pillar sub-region when the reference category contains part of pixel points in any one pillar sub-region;
The image data analysis module obtaining the pillar rectangular salient value based on the edge pixel point of each pillar sub-region and the target straight line comprises the following steps:
taking any one pillar sub-region as an example pillar sub-region, taking the minimum circumscribed rectangle of the example pillar sub-region, respectively selecting edge pixel points closest to four corners of the minimum circumscribed rectangle from edge pixel points of the example pillar sub-region as target edge pixel points, taking the edge pixel points between two adjacent target edge pixel points as a set, and respectively carrying out straight line fitting on the edge pixel points in each set to obtain the fitting goodness of the corresponding set in the example pillar sub-region;
setting a linear coefficient and a constant coefficient, counting the number of target lines in an example pillar subarea as a second number, taking the absolute value of the difference between the second number and the linear coefficient as a number difference, taking the sum of the fitting goodness of each set in the example pillar subarea as a second numerator, taking the sum of the number difference and the constant coefficient as a second denominator to obtain a second ratio as a first result of the example pillar subarea, and taking the sum of the first result of all the pillar subareas as a pillar rectangular significant value;
The method for acquiring the waveform ground subsidence significant value by combining the area of the ore pillar area, the area of the subsidence area, the ore pillar clusters, the subsidence clusters and the ore pillar significant value in the ore area subsidence detection module comprises the following steps:
taking the number of pixel points in a pillar region in a deformation rate diagram as the area of the pillar region and the number of pixel points in a collapse region as the area of the collapse region, counting the number of pillar clusters in the deformation rate diagram as the third number and the number of collapse clusters as the fourth number, taking the area of the collapse region as a third numerator and the area of the pillar region as a third denominator to obtain a third ratio, taking the fourth number as a fourth numerator and the third number as a fourth denominator to obtain a fourth ratio, taking the product of the third ratio and the fourth ratio as a second result, taking the logarithm of the pillar significant value taking a preset value as a base as a third result, and taking the product of the second result and the third result as the wavy ground collapse significant value.
2. The inar-based earth surface image data processing system of claim 1, wherein the acquiring the pillar sub-region of the elevation map and the pillar region and the collapse region of the deformation rate map according to the pillar pixels and the collapse pixels in the image data preprocessing module comprises:
Acquiring pixel points of corresponding positions of each pillar pixel point of the difference value elevation chart in the elevation chart as first pixel points, and taking a region formed by the first pixel points as a pillar sub-region of the elevation chart; respectively acquiring pixel points of corresponding positions of pillar pixels and collapse pixels of a difference elevation chart in a deformation rate chart as a second pixel point and a third pixel point, taking a region formed by the second pixel point as a pillar subarea of the deformation rate chart, taking a region formed by all pillar subareas in the deformation rate chart as a pillar region in the deformation rate chart, and taking a region formed by the third pixel point as a collapse region of the deformation rate chart.
3. The InSAR-based earth surface image data processing system of claim 1, wherein combining the pillar distribution saliency value with the pillar rectangular saliency value in the image data analysis module comprises:
taking the product of the pillar distribution significance value and the pillar rectangle significance value as the pillar significance value.
4. The InSAR-based earth surface image data processing system of claim 1, wherein the determining a ground collapse condition for monitoring the mine based on the undulating ground collapse significance value comprises:
Setting a collapse threshold and a difference threshold, when the collapse significant value of the wavy ground is larger than or equal to the collapse threshold, monitoring that the wavy ground collapses in the mining area, when the collapse significant value of the wavy ground is smaller than the collapse threshold, respectively calculating the average value of the pixel values of all the pixel points in the mining pillar area in the elevation map as the pixel average value of the mining pillar area, and the average value of the pixel values of all the pixel points in the collapse area as the pixel average value of the collapse area, and when the absolute value of the difference value between the pixel average value of the mining pillar area and the pixel average value of the collapse area is larger than or equal to the difference threshold, monitoring that the ground collapses in the mining area.
5. The InSAR-based earth surface image data processing system of claim 1, wherein the dividing pixels in the difference elevation map into pillar pixels and collapse pixels by segmentation thresholds in the image data preprocessing module comprises:
and obtaining a segmentation threshold value by using a maximum inter-class variance method for pixel values of the pixel points in the difference elevation map, classifying the pixel points with the pixel values larger than the segmentation threshold value in the difference elevation map as pillar pixel points, and classifying the pixel points with the pixel values smaller than or equal to the segmentation threshold value in the difference elevation map as collapse pixel points.
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