CN116819521A - Mining subsidence area detection method based on Mask R-CNN model - Google Patents
Mining subsidence area detection method based on Mask R-CNN model Download PDFInfo
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- 238000005065 mining Methods 0.000 title claims abstract description 92
- 238000001514 detection method Methods 0.000 title claims abstract description 43
- 238000004804 winding Methods 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 24
- 230000000694 effects Effects 0.000 claims abstract description 21
- 238000012549 training Methods 0.000 claims abstract description 15
- 238000011156 evaluation Methods 0.000 claims description 13
- 238000010801 machine learning Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 5
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract 1
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000005305 interferometry Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
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- 230000004048 modification Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The application discloses a method for detecting a mining subsidence area based on a mask-CNN model, which comprises the steps of firstly obtaining an original SAR image, then co-registering the original SAR image to a public main image, and carrying out InSAR observation to obtain a winding interference image; slicing the winding interference image based on the winding interference image to generate a color image, and after the ground subsidence feature of the mining area is marked on the slicing color interference image to manufacture a data set, simulating the winding interference image by using a Gaussian surface function and GACOS (gas-phase alternating current) mode to enlarge the data set; based on a Mask R-CNN model, inputting a data set for training to obtain a mining area ground subsidence detection model, detecting a mining subsidence area and determining the position and boundary of the mining subsidence area; after the historical and current mining subsidence areas are obtained, the evolution state thereof is determined, and the surface activity level (activity time and frequency of each mining subsidence area) is evaluated. The application has the advantages of easy operation, good effect, time saving and storage space saving, and is suitable for a wide area range without limiting a using platform.
Description
Technical Field
The application relates to the technical field of soil monitoring, in particular to a mining subsidence area detection method based on a Mask R-CNN model.
Background
The synthetic aperture radar interferometry (InSAR) is an advanced geodetic instrument, has the characteristics of strong all-day and all-weather working capacity, wide space coverage, high spatial resolution, high measurement accuracy, no need of ground instruments and the like, and has become a powerful technology for measuring wide-area earth surface deformation.
In the prior art, mining subsidence area detection methods generally include two types: (i) Setting a deformation ratio threshold, and (ii) using a Machine Learning (ML) algorithm.
For the first type of method for setting the deformation rate threshold, the average deformation rate graph obtained by InSAR superposition or a time sequence algorithm is mainly studied, and although the method is easy to realize, the method cannot detect the region with limited mining subsidence, such as the region with mining subsidence in a limited period. For the second class of methods using Machine Learning (ML) algorithms, more and more ML-based algorithms have been developed to detect mining subsidence areas using SAR interferometry, but this approach is still at an early stage and the technological approach is not mature enough.
Disclosure of Invention
Based on the above, it is necessary to provide a method for detecting a mining subsidence area based on a Mask R-CNN model.
The embodiment of the application provides a mining subsidence area detection method based on a Mask R-CNN model, which comprises the following steps:
acquiring an original SAR image, and track data and terrain data corresponding to the original SAR image;
combining the track data and the topography data, and performing InSAR observation by using two adjacent SAR images to obtain a winding interference image;
creating a dataset from the wrapped interference image;
based on a Mask R-CNN model, training through a data set, and taking a machine learning evaluation index as a model training result evaluation index to obtain an optimal mining area ground subsidence detection model;
inputting the winding interference image into a mining area ground subsidence detection model to obtain the latest mining subsidence area;
after the historical and latest mining subsidence areas are obtained, determining the evolution state of the mining subsidence areas, and evaluating the ground surface activity level;
in addition, the determination of the winding interference image includes the steps of:
interfering the adjacent SAR images to obtain a preliminary interference image;
removing land and terrain phase effects using an external DEM;
removing the effects of track errors using the precision track;
performing multi-view operation on the interference image to suppress noise;
a winding interference image is obtained.
Additionally, the creating a dataset from the wrapped interference image includes the steps of:
modeling the surface deformation caused by mining by using a Gaussian surface function, and obtaining mining subsidence areas with different subsidence amounts, boundary ranges and forms by adjusting parameters;
differentiating an atmospheric delay diagram obtained by the GACOS to simulate an atmospheric phase;
simulating noise phase using a continuously evenly distributed random function;
and adding the obtained earth surface deformation, the atmospheric error and the random noise, and winding to obtain a data set.
In addition, the determination of the mining area ground subsidence detection model comprises the following steps:
the real dataset was read as per 4:1, dividing the Mask R-CNN basic model into a training data set and a verification data set to train the Mask R-CNN basic model;
training a model by increasing iteration times, taking the precision rate, the recall rate, the accuracy rate and the F1 comprehensive index as model precision evaluation indexes, and obtaining a mining area ground subsidence detection model using a real data set when the comprehensive precision reaches the highest;
performing a Mask R-CNN basic model by using the real and simulated mixed data set to obtain a mining area ground subsidence detection model by using the mixed data set;
and comparing the precision evaluation indexes of the mining subsidence detection models using the real data set and the mixed data set to determine a final mining area ground subsidence detection model.
Additionally, the obtaining a mining subsidence area includes:
and executing the same operation as that when the historical interference image is operated, generating a winding interference image together with the latest SAR image in the historical SAR data set, inputting the obtained winding interference image into a mining area ground subsidence detection model, and identifying the latest mining subsidence area.
Additionally, the indicators for evaluating the surface activity level include: the duration and frequency of collapse of the mining subsidence area.
In addition, the duration of collapse of the mining subsidence area is obtained by multiplying the number of collapse times by the time interval for SAR image acquisition.
Compared with the prior art, the mining subsidence area detection method based on the Mask R-CNN model has the following beneficial effects:
the application uses Mask R-CNN as a detection basic model, and uses an original winding interferogram generated from two adjacent SAR images as a data set of the detection model, and provides a method for generating a mining area simulation data set, which is beneficial to improving model accuracy; the mining subsidence detection model based on the Mask R-CNN model is constructed, the mining subsidence area can be effectively detected from the winding interference diagram, the calculation time is saved, the extra uncertainty caused by phase expansion and time sequence inversion is reduced, the method is a novel wide-area mining subsidence detection method which is easy to operate, good in effect and resource saving, wide in application range and free of limitation of using platforms, and the rapid detection and long-term monitoring of mining area ground subsidence are important contents for monitoring mining activities, so that the method is very important for economic development of mining cities and personal and property safety. Meanwhile, the method can be used for guiding underground mining work, promoting supervision of mining activities and timely finding illegal mining.
Drawings
FIG. 1 is a schematic diagram of a mining subsidence detection technique for a method of detecting a mining subsidence area based on a Mask R-CNN model, provided in one embodiment;
FIG. 2 is a simulated data set generation flow chart of a method of detecting a mining subsidence area based upon a Mask R-CNN model, provided in one embodiment;
FIG. 3 is a map of surface activity based on wide area mining subsidence detection results of a method of detecting mining subsidence areas based on a Mask R-CNN model, as provided in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, a method for detecting a mining subsidence area based on a Mask R-CNN model is provided, the method comprising:
as shown in figures 1-3, the application utilizes a Mask R-CNN basic model, uses adjacent two-scene SAR winding interferograms to detect mining subsidence, aims at saving time and storage space, simultaneously has simple operation method, obtains a better mining subsidence detection model, and carries out surface activity evaluation on a mining area on the basis of the model.
Firstly, an original SAR image, track data and terrain data corresponding to the original SAR image are acquired, and only two adjacent SAR images are used for InSAR observation to obtain a winding interference image:
step 1: interfering the registered adjacent SAR images to obtain a preliminary interference image;
step 2: removing land and terrain phase effects using an external DEM;
step 3: removing the effects of track errors using the precision track;
step 4: performing multi-view operation on the interference image to suppress noise;
step 5: the interference image is generated into a color picture.
As shown in fig. 1, a dataset is created from the wrapped interferometry images, the ground subsidence features of the mine are manually marked on the sliced color interferometry images, and the resulting file contains the original image, mask information and marked mask image as a standard dataset format for training the network model.
In order to generate a simulated mining subsidence InSAR winding interferogram according to the time and frequency of activity of each mining subsidence area and the generalization capability of the model, a method is provided as shown in FIG. 2, comprising the steps of:
step 1: modeling the surface deformation caused by mining by using a Gaussian surface function, and obtaining mining subsidence areas with different subsidence amounts, boundary ranges and forms by adjusting parameters;
step 2: differentiating an atmospheric delay diagram obtained by the GACOS to simulate an atmospheric phase;
step 3: simulating noise phase using a continuously evenly distributed random function;
step 4: and (3) adding the surface deformation, the atmospheric error and the random noise obtained in the steps 3-1 to 3-3, and winding to obtain a simulated interference image data set.
Training is carried out by taking a Mask R-CNN model as a basis and a training data set and a test data set, and machine learning evaluation indexes are taken as model training result evaluation indexes, so that a mining area ground subsidence detection model is finally obtained:
step 1: the real dataset was read as per 4:1, dividing the Mask R-CNN basic model into a training data set and a verification data set to train the Mask R-CNN basic model;
step 2: training a model by increasing iteration times, taking the precision rate, the recall rate, the accuracy rate and the F1 comprehensive index as model precision evaluation indexes, and obtaining a mining subsidence detection model using a real data set when the comprehensive precision reaches the highest;
step 3: performing the steps 1 and 2 by using the real and simulated mixed data set to obtain a mining subsidence detection model by using the mixed data set;
step 4: and comparing the precision evaluation indexes of the mining subsidence detection model using the real data set and the mixed data set, and preferentially determining the final mining subsidence detection model.
The winding interferogram is input into a mining area ground subsidence detection model as in fig. 1 to detect mining subsidence areas and determine their location and boundaries. After the historical and current mining subsidence areas are obtained, the evolution state thereof is determined and the surface activity level (time and frequency of activity for each mining subsidence area) is evaluated.
The mining subsidence ground detection model of FIG. 3 may detect mining subsidence areas in the interferogram that are substantially coincident in location and boundary with the detection result by visual recognition.
FIG. 3 shows collapse durations, since mining subsidence area detection is applied to individual interferograms, there is no time overlap, and the time span of each interferogram is known, and thus the cumulative duration of each collapse is determined by summing the time spans of all interferograms for which a particular pixel is identified as "active".
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (7)
1. A method for detecting a mining subsidence area based on a Mask R-CNN model, comprising:
acquiring an original SAR image, and track data and terrain data corresponding to the original SAR image;
and combining the track data and the terrain data, and performing InSAR observation by using two adjacent SAR images to obtain a winding interference image:
creating a dataset from the wrapped interference image;
based on a Mask R-CNN model, training through a data set, and taking a machine learning evaluation index as a model training result evaluation index to obtain an optimal mining area ground subsidence detection model;
inputting the winding interference image into a mining area ground subsidence detection model to obtain the latest mining subsidence area;
after the historical and up-to-date mining subsidence areas are obtained, the evolution state thereof is determined and the surface activity level is evaluated.
2. A method of detecting a mining subsidence area based upon a Mask R-CNN model according to claim 1, wherein the obtaining of the winding interference image includes the steps of:
interfering the adjacent SAR images to obtain a preliminary interference image;
removing land and terrain phase effects using an external DEM;
removing the effects of track errors using the precision track;
performing multi-view operation on the interference image to suppress noise;
a winding interference image is obtained.
3. A method of mining subsidence area based upon a Mask R-CNN model according to claim 1, wherein the creating a dataset from a wrapped interference image comprises the steps of:
modeling the surface deformation caused by mining by using a Gaussian surface function, and obtaining mining subsidence areas with different subsidence amounts, boundary ranges and forms by adjusting parameters;
differentiating an atmospheric delay diagram obtained by the GACOS to simulate an atmospheric phase;
simulating noise phase using a continuously evenly distributed random function;
and adding the obtained earth surface deformation, the atmospheric error and the random noise, and winding to obtain a data set.
4. A method of mining subsidence area detection based on the Mask R-CNN model according to claim 1, wherein the determination of the mining area ground subsidence detection model comprises the steps of:
the real dataset was read as per 4:1, dividing the Mask R-CNN basic model into a training data set and a verification data set to train the Mask R-CNN basic model;
training a model by increasing iteration times, taking the precision rate, the recall rate, the accuracy rate and the F1 comprehensive index as model precision evaluation indexes, and obtaining a mining area ground subsidence detection model using a real data set when the comprehensive precision reaches the highest;
performing a Mask R-CNN basic model by using the real and simulated mixed data set to obtain a mining area ground subsidence detection model by using the mixed data set;
and comparing the precision evaluation indexes of the mining subsidence detection models using the real data set and the mixed data set to determine a final mining area ground subsidence detection model.
5. A method of mining subsidence area detection based on a Mask R-CNN model according to claim 1, wherein the obtaining the mining subsidence area comprises: and executing the same operation as that when the historical interference image is operated, generating a winding interference image together with the latest SAR image in the historical SAR data set, and inputting the obtained winding interference image into a mining area ground subsidence detection model to obtain the latest mining subsidence area.
6. The method for mining subsidence area based upon the Mask R-CNN model of claim 1, wherein the index for evaluating the surface activity level comprises: the duration and frequency of collapse of the mining subsidence area.
7. The method for mining subsidence area detection based on the Mask R-CNN model according to claim 6, wherein the duration of collapse of the mining subsidence area is obtained by multiplying the number of collapse times by the time interval for SAR image acquisition.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310148628.5A CN116819521A (en) | 2023-02-22 | 2023-02-22 | Mining subsidence area detection method based on Mask R-CNN model |
| IE20230126U IE20230126U1 (en) | 2023-02-22 | 2023-04-26 | A detection method for mining subsidence area based on the Mask R-CNN model |
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- 2023-02-22 CN CN202310148628.5A patent/CN116819521A/en active Pending
- 2023-04-26 IE IE20230126U patent/IE20230126U1/en unknown
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| IE20230126U1 (en) | 2023-10-25 |
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