CN113900117A - Underground non-evidence exploitation identification method integrating PS-InSAR and optical remote sensing - Google Patents

Underground non-evidence exploitation identification method integrating PS-InSAR and optical remote sensing Download PDF

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CN113900117A
CN113900117A CN202111070255.1A CN202111070255A CN113900117A CN 113900117 A CN113900117 A CN 113900117A CN 202111070255 A CN202111070255 A CN 202111070255A CN 113900117 A CN113900117 A CN 113900117A
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point
building
deformation
mining
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夏元平
惠振阳
艾金泉
侯靖钥
胡子阳
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East China Institute of Technology
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention discloses an underground non-evidence exploitation identification method fusing PS-InSAR and optical remote sensing, which comprises the following steps: step 1: acquiring a vector outline of a ground surface building by using a high-beam optical satellite sensor; step 2: acquiring deformation information of a PS target data set on the surface of the mine by using a satellite-borne SAR sensor; and step 3: carrying out spatial superposition analysis on the building vector outline extracted from the research area and the deformation information of the acquired PS target data set, and extracting a PS point set in each building; and 4, step 4: and performing space-time characteristic analysis on the deformation difference, the deformation gradient and the accumulated deformation of each building through the extracted time sequence PS point set, screening out the buildings with abnormal deformation, and judging whether the non-evidence exploitation exists.

Description

Underground non-evidence exploitation identification method integrating PS-InSAR and optical remote sensing
Technical Field
The invention belongs to the technical field of underground non-evidence exploitation, and particularly relates to an underground non-evidence exploitation identification method fusing PS-InSAR and optical remote sensing.
Background
At present, D-InSAR differential interferograms are mainly adopted to extract mining subsidence characteristics of the ground surface caused by underground mining, and the subsidence characteristics are used as division criteria to identify underground non-evidence mining. However, when the underground mining amount is small and obvious mining subsidence characteristics are not enough formed on the ground, the characteristics cannot be extracted from the differential interference image, so that the underground non-evidence mining event cannot be accurately identified by the method. Research shows that in order to avoid the detection of law enforcement personnel, part of illegal molecules do not pay any cost, coal mines are dug privately in self-built civil houses, the stealing means is very hidden, the coal mines are seen from the outside to be a large house, and the coal mines enter the house to be a civil environment and are very deceptive.
In view of the fact that the illegal events are mined by shallow coal resources and the buildings on the ground can keep strong and stable radar scattering characteristics in a long time sequence, the method combines the PS-InSAR technology and high-spectroscopic remote sensing to extract micro subsidence information of ground buildings caused by underground mining and analyze the time-space development characteristics of the micro subsidence information, and therefore the method can provide possibility for early identification, key monitoring and effective prevention and control of underground unverified mining events.
Disclosure of Invention
The invention aims to provide an underground non-evidence mining identification method fusing PS-InSAR and optical remote sensing aiming at the existing problems.
The technical scheme adopted by the invention is as follows:
an underground non-evidence mining identification method fusing PS-InSAR and optical remote sensing comprises the following steps:
step 1: acquiring a vector outline of a ground surface building by using a high-beam optical satellite sensor;
step 2: acquiring deformation information of a PS target data set on the surface of the mine by using a satellite-borne SAR sensor;
and step 3: carrying out spatial superposition analysis on the building vector outline extracted from the research area and the deformation information of the acquired PS target data set, and extracting a PS point set in each building;
and 4, step 4: and performing space-time characteristic analysis on the deformation difference, the deformation gradient and the accumulated deformation of each building through the extracted time sequence PS point set, screening out the buildings with abnormal deformation, and judging whether the non-evidence exploitation exists.
Preferably, in step 1, the specific process of acquiring the surface building vector profile by using the hyperspectral satellite sensor is as follows:
step 101: firstly, preprocessing optical remote sensing image data acquired by a high-spectrum optical satellite sensor;
step 102: then, carrying out self-adaptive cutting on the preprocessed remote sensing image to be extracted, sending the remote sensing image cut into sub-areas to a SegNet network model, and classifying pixels of each sub-area in sequence through the SegNet network model;
step 103: and finally, on the basis of fusing the feature extraction results of all the sub-regions, extracting the vector outline of the building element in the remote sensing image by utilizing a CRF smoothing method through analyzing the outline regularization condition of the building element.
Preferably, in step 101, the preprocessing includes performing gas orthorectification, radiometric calibration, image registration, image fusion, image denoising, and enhancement processing on the optical remote sensing image.
Preferably, in step 2, a specific process of acquiring deformation information of the mine surface PS target data set by using the satellite-borne SAR sensor is as follows:
step 201: after M SAR images acquired in the research area range are subjected to differential processing, M-1 differential interference image pairs can be obtained, and for a certain resolution unit in the images, the average value of the amplitude (M) in the SAR image sequenceA) And amplitude deviation index (D)A) Can be respectively expressed as:
Figure BDA0003259948340000031
in the formula, miIs the amplitude value, sigma, of the pel in the ith imageAIs the standard deviation of the time sequence amplitude; for a target with high signal-to-noise ratio, the phase noise level can be equivalently measured by using an amplitude dispersion index, so that when the pixel on the high signal-to-noise ratio meets the condition of formula (1)Then, the pixel can be identified as the PS target, namely:
Figure BDA0003259948340000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003259948340000033
is the average amplitude value of the average amplitude image,
Figure BDA0003259948340000034
is amplitude dispersion index threshold; when the condition is satisfied
Figure BDA0003259948340000035
Then, resolution cells with high amplitude mean have higher coherence; when the dispersion index threshold is smaller than the given threshold, the smaller the amplitude dispersion index value of the resolution unit is, the more stable the target point is;
step 202: according to the detected PS target and the differential interference phase time sequence thereof, a phase model is constructed, deformation components and error components of the PS target are calculated to obtain residual phases, atmospheric phase values and residual terrain phases in the differential interference phases are estimated and removed through time-space filtering, and finally deformation detection results of the surface PS target data set are obtained.
Preferably, in step 4, the screening process of the abnormally deformed building is as follows:
suppose that there are n building PS points P in the research areaiI is 1,2, …, n, m characteristic points with abnormal or obvious subsidence are found out,
step 401: calculating the difference of the settlement of each PS point before and after the first time, i.e.
Figure BDA0003259948340000041
Step 402: obtaining an initial settlement seed point set:
Figure BDA0003259948340000042
where S is a set of n points and Δ is the sedimentation threshold, set herein as a constant;
step 403: sequentially traversing each point in the initial settlement point set Seed to obtain a neighboring point set of each point within the range of the distance d, wherein the formula is as follows:
Figure BDA0003259948340000043
in the formula (I), the compound is shown in the specification,
Figure BDA0003259948340000044
is piA set of neighboring points of the point or points,
Figure BDA0003259948340000045
is piCoordinates of the points in the x and y directions, wherein m is the number of the seed point sets;
step 404: calculating the average gradient change rate of each point in the initial settlement point set Seed
Figure BDA0003259948340000046
Figure BDA0003259948340000047
The formula is expressed as follows:
Figure BDA0003259948340000048
in the formula, pkIs a point piOne point in a set of neighboring points, i.e.
Figure BDA0003259948340000049
Figure BDA00032599483400000410
Is a point piNumber of sets of neighboring points.
Figure BDA00032599483400000411
And
Figure BDA00032599483400000412
respectively represents piPoint sum pkThe amount of point settlement variation;
step 405: traversing each point in the initial settlement point set Seed, if the average gradient change rate of the point is greater than a threshold eta, the point is a settlement change abnormal point, and the formula is as follows:
Figure BDA00032599483400000413
in the formula, the change rate threshold η may be adaptively calculated according to the average gradient change rate of each sub-point in the measurement area, and is specifically calculated as follows:
η＝mean(gradientp)+std(gradientp) (8)
in the formula, mean (-) represents the average value of the average gradient change rates of various sub-points in the area, std (-) is the corresponding standard deviation;
step 406: sequentially calculating the accumulated settlement of each point in the Set point Set in the long-time monitoring process, and determining the point with the accumulated settlement larger than a threshold value as a final PS point of the abnormally-deformed building, wherein the formula is as follows:
Figure BDA0003259948340000051
in the formula (t)0,t1) For monitoring the time interval, Δ Z is the cumulative sedimentation change threshold.
The invention has the beneficial effects that: the underground non-evidence mining identification method fusing the PS-InSAR and the optical remote sensing has good reliability and applicability, and after the identification precision is evaluated, the accuracy rate of a local area is 40 percent, and the detection rate is 66.67 percent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is an interactive diagram of main objects of a model for uncertified mining identification by combining optical remote sensing and PS-InSAR technology.
Fig. 2 is a flow chart illustrating the building element extraction.
Fig. 3 is a graph showing a PS-based SAR image time-series analysis.
Fig. 4 is a flowchart of a method for uncertified mining identification by combining optical remote sensing and PS-InSAR technologies according to the present invention.
Fig. 5 is a flowchart of a method for detecting PS points of an abnormally deformed building.
FIG. 6 is a graph showing the results of PALSAR interference.
FIG. 7 shows a PALSAR image unwrapping interferogram.
FIG. 8 is a time series diagram of changes in the mountain bottom village.
Fig. 9 shows images (a) of QuickBird02 in research area 2008 and residential extraction results (b).
Fig. 10 shows a Worldview02 image (a) in the research area 2010 and a residential area extraction result (b).
Fig. 11 shows the result of extracting the contour of the residential area in 2008 in the research area.
Fig. 12 shows the extraction result of the contour of the residential area in 2010 in the research area.
Fig. 13 shows the result of extracting PS deformation point sets in the mountainous bottom village residential areas.
Fig. 14 shows the initial set of settled seed points extraction results.
Fig. 15 is a diagram showing the detection result of a suspected illegal mining site.
Fig. 16 is a sectional view of an illegal coal mining site No. 3 in submountainous village.
FIG. 17 is a plan view showing the resource reserve estimation of No. 15 coal seam damaged by No. 3 illegal coal mining points.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
According to the main classes and the mutual relations of the mining surface building subsidence space-time characteristic analysis model, the fact that the real-time data source of the mining surface-time process mainly depends on the satellite-borne SAR and the optical satellite sensor which are observed on the ground and the building underground mining events can be obtained, when a new underground mining event or a mining surface is pushed, the mining surface image can generate geological events with corresponding levels according to the underground mining position and range, the generated geological events are sent to the mining subsidence geological space-time process, the space-time process sends the events to the mining surface buildings in an affected area, then the subsidence space-time characteristics of the mining surface buildings are analyzed, whether the driving of the underground non-evidence mining event is responded is determined, and the interaction of the main geological objects and the events is shown in figure 1.
According to the interactive map, the mine surface object generates events of corresponding levels when the surface deformation state meets corresponding conditions, the latest events of the mine surface in the area need to be continuously informed in the geological time-space process along with the continuous advancing of underground mining events, the mine surface can be used for extracting the subsidence information of the mine surface buildings by combining the surface subsidence information obtained by the PS-InSAR technology after receiving the building contour extracted by high-spectroscopic remote sensing, and then mining subsidence events are generated by analyzing the subsidence time-space characteristics of the buildings and judging whether the mining subsidence events exist in an uncertified mode. And if the non-evidence mining event is confirmed, carrying out early warning forecast response on the event. And after the mine right range object receives the uncertified mining event, the illegal mining event in and out of the mine right range can be further discriminated by combining the spatial position relation.
To this end, the invention specifically provides an underground non-evidence mining identification method fusing PS-InSAR and optical remote sensing, as shown in FIG. 4, comprising the following steps:
step 1: acquiring a vector outline of a ground surface building by using a high-beam optical satellite sensor;
step 2: acquiring deformation information of a PS target data set on the surface of the mine by using a satellite-borne SAR sensor;
and step 3: carrying out spatial superposition analysis on the building vector outline extracted from the research area and the deformation information of the acquired PS target data set, and extracting a PS point set in each building;
and 4, step 4: and performing space-time characteristic analysis on the deformation difference, the deformation gradient and the accumulated deformation of each building through the extracted time sequence PS point set, screening out the buildings with abnormal deformation, and judging whether the non-evidence exploitation exists.
1. Mining area building element extraction based on optical remote sensing
The surface buildings (residential areas) of mining areas are important places for production and life of people, but some illegal molecules of national mineral resources are stolen, and in order to avoid the inspection of mine law enforcement personnel, coal mines are excavated from the well mouths to the underground in self-built civil houses, so that not only is the benefit of the country damaged, but also a series of potential safety hazards are easily caused. How to extract the surface buildings of the mining area quickly and grasp the information of subsidence characteristics, distribution status and the like, and can provide scientific basis for related departments to quickly check and locate underground illegal mining events. Therefore, in order to improve the quality and efficiency of extracting thematic elements of the surface buildings of the mining area and meet the requirement of efficient identification of underground illegal mining, the surface buildings of the mining area are taken as main research objects for typical surface building elements in the optical remote sensing image, and pixel-level building elements are extracted based on the depth convolution characteristics.
The technical flow of the method for extracting the building based on the depth convolution characteristic of the optical remote sensing image is shown in figure 2, and comprises the following steps:
step 101: firstly, preprocessing optical remote sensing image data acquired by a high-spectrum optical satellite sensor;
step 102: then, carrying out self-adaptive cutting on the preprocessed remote sensing image to be extracted, sending the remote sensing image cut into sub-areas to a SegNet network model, and classifying pixels of each sub-area in sequence through the SegNet network model;
step 103: and finally, on the basis of fusing the feature extraction results of all the sub-regions, extracting the vector outline of the building element in the remote sensing image by utilizing a CRF smoothing method through analyzing the outline regularization condition of the building element.
In step 101, the preprocessing includes performing gas orthorectification, radiometric calibration, image registration, image fusion, image denoising, and enhancement processing on the optical remote sensing image.
2. Mining area surface deformation PS-InSAR monitoring
The time series analysis of SAR images based on the PS point target is shown in fig. 3. Firstly, reflection information of the surface PS point is obtained through single observation, and the space distance between the SAR satellite and the PS point can be calculated. If the ground PS point is deformed during the repeated observation period of the radar, the deformation information of the ground PS point target can be measured according to the moving deformation during the two-time observation period by repeatedly observing the ground in the same range. Therefore, the key to processing and analyzing multi-temporal SAR data is the detected PS targets with stable radar wave scattering properties and high coherence. The specific process for detecting the PS point target is as follows:
step 201: after M SAR images acquired in the research area range are subjected to differential processing, M-1 differential interference image pairs can be obtained, and for a certain resolution unit in the images, in the SAR, the images are subjected to differential processingAmplitude mean (m) in image sequencesA) And amplitude deviation index (D)A) Can be respectively expressed as:
Figure BDA0003259948340000091
in the formula, miIs the amplitude value, sigma, of the pel in the ith imageAIs the standard deviation of the time sequence amplitude; for a target with a high signal-to-noise ratio, the phase noise level of the target can be equivalently measured by using an amplitude dispersion index, so that when a pixel on the high signal-to-noise ratio meets the condition of the formula (1), the pixel can be determined as a PS target, namely:
Figure BDA0003259948340000092
in the formula (I), the compound is shown in the specification,
Figure BDA0003259948340000093
is the average amplitude value of the average amplitude image,
Figure BDA0003259948340000094
is amplitude dispersion index threshold; when the condition is satisfied
Figure BDA0003259948340000101
Then, resolution cells with high amplitude mean have higher coherence; when the dispersion index threshold is smaller than the given threshold, the smaller the amplitude dispersion index value of the resolution unit is, the more stable the target point is;
step 202: according to the detected PS target and the differential interference phase time sequence thereof, a phase model is constructed, deformation components and error components of the PS target are calculated to obtain residual phases, atmospheric phase values and residual terrain phases in the differential interference phases are estimated and removed through time-space filtering, and finally deformation detection results of the surface PS target data set are obtained.
3. Underground non-evidence mining identification method based on building subsidence space-time characteristics
In a PS point set of buildings in an adjacent range, PS points of surface buildings sunk due to underground mining have specific abnormal deformation characteristics, and the conclusion of the deformation characteristics is helpful for automatically screening the PS points of the buildings sunk due to underground mining from the PS point set in a larger range, so that the aim of quickly and accurately detecting suspected illegal underground mining points from the sinking information of the buildings (houses) with larger coverage range is fulfilled. That is, in the set of adjacent points within a set distance range, the space-time characteristics of the PS points of these abnormally deformed buildings are mainly expressed as the following three points: firstly, in short-time monitoring, the difference value of the settlement amount of the abnormally deformed building before and after the PS point is relatively large, namely the settlement rate is also larger; and secondly, compared with the normal building shape change point, the average gradient change rate of the PS point of the abnormal deformation building is relatively larger. Thirdly, aiming at long-time monitoring, the accumulated deformation quantity of the PS point of the abnormally deformed building is relatively large.
Based on the three characteristics, the method mainly adopts a gradual progressive mode to detect the PS points of the abnormally deformed building, and the flow chart of the method is shown in FIG. 5. The method comprises the steps of firstly, calculating settlement amount of each PS point twice before and after in short-time monitoring, and taking the PS point with relatively large settlement amount as an abnormal PS point candidate point set; and traversing each PS point in the candidate point set, calculating the gradient change rate of each point, and removing the PS points with smaller change rate from the abnormal candidate point set. And finally, calculating the accumulated settlement variation of each point in the candidate point set, and determining the point with the larger variation as the PS point of the abnormal deformation building.
Namely, suppose that there are n building PS points P in the research area rangeiI is 1,2, …, n, and m characteristic points with abnormal or obvious subsidence are found out.
Step 401: calculating the difference of the settlement of each PS point before and after the first time, i.e.
Figure BDA0003259948340000111
Step 402: obtaining an initial settlement seed point set:
Figure BDA0003259948340000112
where S is a set of n points and Δ is the sedimentation threshold, set herein as a constant;
step 403: sequentially traversing each point in the initial settlement point set Seed to obtain a neighboring point set of each point within the range of the distance d, wherein the formula is as follows:
Figure BDA0003259948340000113
in the formula (I), the compound is shown in the specification,
Figure BDA0003259948340000114
is piA set of neighboring points of the point or points,
Figure BDA0003259948340000115
is piCoordinates of the points in the x and y directions, wherein m is the number of the seed point sets;
step 404: calculating the average gradient change rate of each point in the initial settlement point set Seed
Figure BDA0003259948340000116
Figure BDA0003259948340000117
The formula is expressed as follows:
Figure BDA0003259948340000118
in the formula, pkIs a point piOne point in a set of neighboring points, i.e.
Figure BDA0003259948340000119
Figure BDA00032599483400001110
Is a point piNumber of sets of neighboring points.
Figure BDA00032599483400001111
And
Figure BDA00032599483400001112
respectively represents piPoint sum pkThe amount of point settlement variation;
step 405: traversing each point in the initial settlement point set Seed, if the average gradient change rate of the point is greater than a threshold eta, the point is a settlement change abnormal point, and the formula is as follows:
Figure BDA0003259948340000121
in the formula, the change rate threshold η may be adaptively calculated according to the average gradient change rate of each sub-point in the measurement area, and is specifically calculated as follows:
η＝mean(gradientp)+std(gradientp) (8)
in the formula, mean (-) represents the average value of the average gradient change rates of various sub-points in the area, std (-) is the corresponding standard deviation;
step 406: sequentially calculating the accumulated settlement of each point in the Set point Set in the long-time monitoring process, and determining the point with the accumulated settlement larger than a threshold value as a final PS point of the abnormally-deformed building, wherein the formula is as follows:
Figure BDA0003259948340000122
in the formula (t)0,t1) For monitoring the time interval, Δ Z is the cumulative sedimentation change threshold.
Therefore, the PS point set of the deformation of the surface building extracted based on the optical remote sensing and the PS-InSAR technology can more accurately screen the PS point of abnormal deformation from the PS point set of the building by summarizing the subsidence space-time characteristic rule of the PS point set of the surface building, and the abnormal deformation of the surface building has a certain spatial corresponding relation with the underground mining activity, so that the underground illegal mining event without evidence can be further identified by combining the deformation characteristic of the PS point of the building and the spatial relation of a mined-out area below the PS point of the surface building.
Example analysis and verification
(1) Overview of the region of investigation
The mineral resources of Shanxi province are rich, the distribution is wide, the coal seam is thick, it is the coal production base of our country. According to statistics, the coal resources of Shanxi provinces account for about one third of the total amount of China, and the coal resources account for about 39.6% of the soil area of Shanxi countries. Yangquan city is located in the northeast of Qin water coal field in Shanxi province, and is the largest anthracite production base in China. Due to the characteristics of simple geological structure, easy mining, shallow burying, low mining cost and the like, the phenomenon of local private excavation and excessive mining is abused, and although local governments can carry out strict actions for striking illegal mining specialties and detailed investigation of various illegal mining behaviors, particularly illegal hole mining behaviors, the phenomenon of illegal mining still happens occasionally. For example, during 2011-2012, illegal mining molecular organization personnel steal and mine coal resources in a residential quarter of a village in the front suburb of the Yangquan by using tools such as an electric pick in a hole mining mode, and mine more than 100 tons of coal, but the total quantity of coal resources damaged and mined due to illegal coal mining is 3000 tons, and the value of the damaged coal resources is up to 116 ten thousand yuan; during the period from 2012 to 2013, illegal mining molecules steal more than 600 tons of coal and carbon resources in resident houses of great spring villages in the suburbs of the spring cities in the same hole mining mode[204]. Therefore, in order to verify the effectiveness and reliability of identifying the underground unlicensed illegal mining event by combining the PS-InSAR technology and high-spectroscopic remote sensing, the mountain bottom village of the suburb river bottom of the Yangquan city in Shanxi province is selected as a main research area in the analysis of the example.
(2) PALSAR data
The topography in the spring is complicated, the weather is changeable, the earth's surface vegetation covers kind abundantly, arbor and shrub grass are complete, and receive the influence of season seriously, easily cause the interference pattern to lose the coherent phenomenon. Therefore, in the practical application of the illegal mining identification in the Yangquan region, the requirement for high-precision observation of the deformation of the earth surface of the mining area is firstly met, and the precision and the capability of monitoring the subsidence information of the earth surface of the mining area are influenced by parameters such as the wavelength, the ground resolution and the like of radar image data, so that the selection of a proper SAR data source is very important. In a conventional satellite-borne SAR satellite, the PALSAR satellite data of the L waveband has stronger phase-preserving capability due to longer wavelength and 10m spatial resolution, and can better reduce the influence of incoherent and discontinuous phase.
In view of the advantages of strong penetrability and wide-range space coverage of ALOS satellite PALSAR data, 20 scenes of PALSAR data acquired from 12/1/2008 to 5/6/2011 are selected to study settlement information of the surface of the mineral area in the Yangquan. The specific parameter information such as the imaging mode and polarization mode of each scene data in the research area is shown in table 1.
TABLE 1 PALSAR data parameter information
Figure BDA0003259948340000131
Figure BDA0003259948340000141
(3) Optical remote sensing image
In order to separate deformation information of a building (residential area) target from a PS target data set acquired on the ground surface of a research area, the research acquires high-resolution data of an archived QuickBird02 on 9/20 th in 2008 and Worldview02 on 11 th in 10/2010 in Yangquan region in Shaanxi province to extract a building vector contour on the ground surface of a mining area, and data parameters of the building vector contour are respectively shown in tables 2 and 3.
TABLE 2 Quickbird02 image data parameters
Figure BDA0003259948340000142
TABLE 3 Worldview02 image data parameters
Table 5-3 Data parameters of Worldview02 image
Figure BDA0003259948340000143
Figure BDA0003259948340000151
(4) DEM data
The SRTM data includes data in a variety of formats and with different precisions, with different resolutions, such as 30m, 90m, and 900m[205]. In order to ensure the spatial resolution of experimental data and reduce errors introduced by external DEM data to results, external SRTM DEM data with the spatial resolution of 30m is selected in the experiment to carry out interference processing on the PALSAR image and remove the terrain phase, and meanwhile, in view of the fact that the spatial resolution of the image data can be reduced by multi-view processing, a multi-view coefficient of 1:2 is adopted in imaging processing of the PALSAR satellite data.
PS-InSAR settlement information acquisition
The permanent scatterer synthetic aperture radar interferometry is adopted to monitor the residential terrain variation information of a research area, an SAR image with the imaging date of 20090103 is selected as a public main image to form 20 pairs of interference pairs, wherein the shortest time base line is 46 days, the shortest interference pair space base line is 109m, the detailed information of the length of the base line of data and the like is shown in table 4.
TABLE 4 relative combination of interference
Figure BDA0003259948340000152
Figure BDA0003259948340000161
Then, the above interference pairs are subjected to differential interference processing, and the obtained 20 PALSAR interferograms are shown in fig. 6. And then, eliminating the terrain phase information caused by image processing by adopting external DEM data, acquiring clearer differential interference fringes by adopting a self-adaptive filtering method, then performing phase unwrapping by utilizing three-dimensional phase unwrapping, and eliminating the phase trend by adopting a cubic polynomial model to obtain an unwrapped interference pattern as shown in figure 7.
FIG. 12 is a time series chart showing the river bottom village in the river bottom between 29 th day in 2006 and 1 st and 9 th day in 2011. The accumulated deformation amount of each deformation map is the reference time of day 29/12/2006, and the date of the lower right corner of each map is the time corresponding to the imaging of each scene SAR image.
Optical image extraction building outline
Firstly, preprocessing operations such as atmospheric correction, geometric correction and data fusion are carried out on the acquired Quickbird02 and Worldview02 images. And all data is resampled to a spatial resolution of 0.5 meters in order to have a uniform spatial resolution for both views. On the basis of image preprocessing, feature elements in the image are subjected to characteristic analysis, a residential area sample library is constructed, pixel-level residential areas are extracted from a semantic segmentation angle by utilizing depth convolution characteristics, scale parameters, shape parameters and compactness parameters used in image segmentation are 40, 0.6 and 0.5 respectively, original images of Quickbird02 in 2008 and Worldview02 in 2010 in research areas and residential area extraction results are shown in FIGS. 9 and 10 respectively, and the accuracy of automatic extraction of the residential areas in 2008 and 2010 is 90.5% and 91.2% respectively.
And finally, performing network reasoning and smoothing processing on the extracted residential area according to a contour regularization method by analyzing contour regularization conditions and principles of the residential area elements. Meanwhile, in order to better display the contour of the residential area, on the basis of automatic extraction, the automatic extraction result is optimized by adopting a visual correction method for buildings with obvious errors, and the contour extraction results of the residential area in the research areas in 2008 and 2010 are respectively shown in fig. 11 and 12.
Underground illegal mining identification and analysis
Vector outlines of 2008 and 2010 mountain bottom village residential area elements are extracted from the high-resolution remote sensing images, earth surface house targets can be separated from a PS target data set detected by using a PS-InSAR technology by calling a spatial analysis tool in ArcGIS, a PS point set of each residential area in a research area can be extracted, a PS point set in the range of mountain bottom village boundaries is reserved, and finally PS deformation point set information of the mountain bottom residential areas from 2006, 12 and 29 days to 2011, 1 and 9 days is extracted as shown in FIG. 13.
And then, by a method of gradually detecting the PS points of the abnormally-deformed building, firstly calculating the difference value of the settlement amount of each point monitored twice before and after the time sequence of the research area, acquiring an initial settlement seed point set in each adjacent time sequence period according to a set settlement threshold value, wherein the initial settlement seed point set is shown in FIG. 14, and the date of the upper left corner of each graph corresponds to the imaging time of each SAR image. In view of the facts that illegal mining events are activities which are carried out under a building in a concealed mode through hole mining, the mining depth is shallow, the mining depth is about 6m generally, underground illegal mining activities are easy to spread to the ground surface, when underground coal is mined continuously, deformation of the building on the ground surface is induced, in addition, the PALSAR data used for monitoring are long in round-trip period, the deformation amount monitored in each period is larger, and therefore the settlement threshold value is set to be 10mm in an experiment according to the overall condition of monitoring data of a research area.
According to the extracted result of the PS deformation point set of the residential areas of the mountain bottom village, sequentially traversing each point in the initial settlement seed point set to obtain a neighboring point set of each point within a distance range of 100m, calculating the average gradient change rate of each point in the initial settlement point set, and if the average gradient change rate of the point is greater than a threshold eta, the point is a primary settlement change abnormal point, wherein the change rate threshold eta can be calculated according to an average gradient change rate self-adaptive formula (8) of each sub-point in the measuring area range. And finally, sequentially calculating the accumulated settlement of each point in the settlement change abnormal point set in the monitoring process of a certain time sequence, and determining the points with the accumulated settlement more than 80mm in a time sequence as final PS points of the abnormally deformed building, wherein the points can be regarded as suspected illegal mining points, and the spatial position distribution of the points is shown in figure 15. The determination of the accumulated settlement threshold is mainly based on the maximum average deformation empirical value of the surface building caused by non-underground mining in the monitoring time interval research range.
According to the change situation of the adjacent deformation difference values of the suspected illegal mining points, the illegal mining time can be deduced. The presumed mining time of the suspected illegal coal mining point No. 1 is from 12 months to 1 month of 2018 in 2006, the presumed mining time of the suspected illegal coal mining point No. 2 is from 2 months to 7 months in 2007, the presumed mining time of the suspected illegal coal mining point No. 3 is from 10 months to 1 month in 2011 in 2009, the presumed mining time of the suspected illegal coal mining point No. 4 is from 2 months to 4 months in 2009, and the presumed mining time of the suspected illegal coal mining point No. 5 is from 2 months to 4 months in 2010.
In order to verify the reliability and the applicability of a detection result, the historical data of illegal coal mining in the mountain bottom village is consulted by a local national and soil resource supervision department, the historical data of illegal mining and the detected illegal coal mining points are compared and analyzed, 3 illegal coal mining points are obtained by historical investigation in the period from 2006 to 12 to 2011, and except that three detected suspected illegal coal mining points No. 1,2 and 5 have no record of the historical investigation, the suspected illegal coal mining points No. 3 and 4 have illegal mining phenomena in corresponding periods. For example, between 12 months and 2 months in 2007, illegal mining molecules of coal mines in the private houses of No. 4 coal mining sites are organized to illegally mine national coal resources by using tools such as electric picks and tricycles in a hole mining mode, and the illegal mining molecules are searched by local national resource monitoring departments in 2009 and 3 months, so that the total quantity of damaged coal mined resources is 9000 tons, and the national economic loss of nearly 400 ten thousand yuan is caused. The cross-sectional view of the illegal coal mining point No. 3 in the mountain bottom village is detected is shown in a figure 16, illegal mining molecules of the coal mine are mined in a hole mining mode in a house, the elevation of a mining opening is 848.633m, the number of a coal seam where the coal mine is mined is No. 15, the coal-seeing point at the deepest part of the mined No. 15 coal seam is No. 5, and the elevation of a bottom plate is 838.437 m.
FIG. 17 is a plan view of an estimate of the resource reserve of the illegal coal seam failure No. 15 coal seam with the vertical axis position (4210531.655, 38457885.871) below the national 80 coordinate system, where the abscissa 38 represents the band number of the 3 degree band. The elevation of No. 3 coal point is 841.759m, and the floor elevation of No. 5, No. 6 and No. 7 coal point is 838.437m, 839.326m and 838.754m respectively.
476 tons of coal were illegally mined from the illegal mining site during the period of 8 months from 2009 to 10 months from 2010, whichThe area of the middle mining block is 170m2The thickness of the coal bed is 2m, and the apparent density is 1.4t/m3The recovery rate of coal mining is 100%. And the range of illegal mining to destroy No. 15 coal seam is 2417m2The thickness of the coal seam is 5.38m, and the apparent density is 1.4t/m3The coal mining recovery rate is 75%, the total amount of the coal resources which can be mined by the damaged coal is 13654 tons, and the economic value of the coal resource damage is as high as more than 500 ten thousand yuan.
By comparing and analyzing the detected illegal coal mining points of the mountain bottom village from 29 days 12 and 29 days 2006 to 9 days 1 and 9 days 2011 with the historical scout data, 2 illegal coal mining points in 3 detected historical scout can be detected, 2 detected 5 suspected illegal coal mining points are proved to be illegal coal mining points in the historical scout, the accuracy of a local area is 40%, the detection rate is 66.67%, the detection result is basically consistent with the actual situation, and the mining time is basically consistent. However, the detected position of the illegal coal mining point has a deviation of about 50m from the actual mining position, which is mainly because the detected PS point of the abnormally deformed building is positioned above the deepest coal mining point, not the mining opening, and a certain distance is left from the mining opening to the mined coal bed through the mining roadway, so the distance deviation of the detected PS point of the abnormally deformed building in the spatial position is reasonable. Therefore, the analysis and verification of the examples show that the method is feasible and has certain engineering applicability and practical application value.
The reliability and the applicability of the method are verified through the example analysis of illegal mining identification in suburban suburb suburbs of Yangquan of Shanxi province, the identification precision is evaluated, and the accuracy rate of a local area is 40% and the detection rate is 66.67%.
The above description is only for the purpose of illustrating the technical solutions of the present invention and not for the purpose of limiting the same, and other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (5)

1. An underground non-evidence mining identification method fusing PS-InSAR and optical remote sensing is characterized by comprising the following steps:
step 1: acquiring a vector outline of a ground surface building by using a high-beam optical satellite sensor;
step 2: acquiring deformation information of a PS target data set on the surface of the mine by using a satellite-borne SAR sensor;
and step 3: carrying out spatial superposition analysis on the building vector outline extracted from the research area and the deformation information of the acquired PS target data set, and extracting a PS point set in each building;
and 4, step 4: and performing space-time characteristic analysis on the deformation difference, the deformation gradient and the accumulated deformation of each building through the extracted time sequence PS point set, screening out the buildings with abnormal deformation, and judging whether the non-evidence exploitation exists.
2. The underground uncertified mining identification method fusing the PS-InSAR and the optical remote sensing according to claim 1, characterized in that in step 1, the specific process of acquiring the vector outline of the surface building by using the high-spectroscopic satellite sensor is as follows:
step 101: firstly, preprocessing optical remote sensing image data acquired by a high-spectrum optical satellite sensor;
step 102: then, carrying out self-adaptive cutting on the preprocessed remote sensing image to be extracted, sending the remote sensing image cut into sub-areas to a SegNet network model, and classifying pixels of each sub-area in sequence through the SegNet network model;
step 103: and finally, on the basis of fusing the feature extraction results of all the sub-regions, extracting the vector outline of the building element in the remote sensing image by utilizing a CRF smoothing method through analyzing the outline regularization condition of the building element.
3. The underground evidence-free mining identification method fusing PS-InSAR and optical remote sensing as claimed in claim 2, wherein in step 101, the preprocessing comprises performing gas orthorectification, radiometric calibration, image registration, image fusion, image denoising and enhancement processing on the optical remote sensing image.
4. The underground non-evidence mining identification method fusing the PS-InSAR and the optical remote sensing according to claim 1, characterized in that in step 2, the specific process of obtaining the deformation information of the PS target data set on the mine surface by using the satellite-borne SAR sensor is as follows:
step 201: after M SAR images acquired in the research area range are subjected to differential processing, M-1 differential interference image pairs can be obtained, and for a certain resolution unit in the images, the average value of the amplitude (M) in the SAR image sequenceA) And amplitude deviation index (D)A) Can be respectively expressed as:
Figure FDA0003259948330000021
in the formula, miIs the amplitude value, sigma, of the pel in the ith imageAIs the standard deviation of the time sequence amplitude; for a target with a high signal-to-noise ratio, the phase noise level of the target can be equivalently measured by using an amplitude dispersion index, so that when a pixel on the high signal-to-noise ratio meets the condition of the formula (1), the pixel can be determined as a PS target, namely:
Figure FDA0003259948330000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003259948330000023
is the average amplitude value of the average amplitude image,
Figure FDA0003259948330000024
is amplitude dispersion index threshold; when the condition is satisfied
Figure FDA0003259948330000025
Then, resolution cells with high amplitude mean have higher coherence; when the dispersion index threshold is less than the given valueWhen the threshold value is reached, the smaller the amplitude dispersion index value of the resolution unit is, the more stable the target point is;
step 202: according to the detected PS target and the differential interference phase time sequence thereof, a phase model is constructed, deformation components and error components of the PS target are calculated to obtain residual phases, atmospheric phase values and residual terrain phases in the differential interference phases are estimated and removed through time-space filtering, and finally deformation detection results of the surface PS target data set are obtained.
5. The underground non-evidence mining identification method fusing the PS-InSAR and the optical remote sensing according to claim 1, characterized in that in step 4, the screening process of the abnormally deformed building is as follows:
suppose that there are n building PS points P in the research areaiI is 1,2, …, n, m characteristic points with abnormal or obvious subsidence are found out,
step 401: calculating the difference of the settlement of each PS point before and after the first time, i.e.
Figure FDA0003259948330000031
Step 402: obtaining an initial settlement seed point set:
Figure FDA0003259948330000032
where S is a set of n points and Δ is the sedimentation threshold, set herein as a constant;
step 403: sequentially traversing each point in the initial settlement point set Seed to obtain a neighboring point set of each point within the range of the distance d, wherein the formula is as follows:
Figure FDA0003259948330000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003259948330000034
is piA set of neighboring points of the point or points,
Figure FDA0003259948330000035
is piCoordinates of the points in the x and y directions, wherein m is the number of the seed point sets;
step 404: calculating the average gradient change rate of each point in the initial settlement point set Seed
Figure FDA0003259948330000036
Figure FDA0003259948330000037
The formula is expressed as follows:
Figure FDA0003259948330000038
in the formula, pkIs a point piOne point in a set of neighboring points, i.e.
Figure FDA0003259948330000039
Figure FDA00032599483300000310
Is a point piNumber of sets of neighboring points.
Figure FDA00032599483300000311
And
Figure FDA00032599483300000312
respectively represents piPoint sum pkThe amount of point settlement variation;
step 405: traversing each point in the initial settlement point set Seed, if the average gradient change rate of the point is greater than a threshold eta, the point is a settlement change abnormal point, and the formula is as follows:
Figure FDA0003259948330000041
in the formula, the change rate threshold η may be adaptively calculated according to the average gradient change rate of each sub-point in the measurement area, and is specifically calculated as follows:
η＝mean(gradientp)+std(gradientp) (8)
in the formula, mean (-) represents the average value of the average gradient change rates of various sub-points in the area, std (-) is the corresponding standard deviation;
step 406: sequentially calculating the accumulated settlement of each point in the Set point Set in the long-time monitoring process, and determining the point with the accumulated settlement larger than a threshold value as a final PS point of the abnormally-deformed building, wherein the formula is as follows:
Figure FDA0003259948330000042
in the formula (t)0,t1) For monitoring the time interval, Δ Z is the cumulative sedimentation change threshold.
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