CN112857310A - Sedimentation monitoring method based on D-InSAR technology and image weighted superposition - Google Patents
Sedimentation monitoring method based on D-InSAR technology and image weighted superposition Download PDFInfo
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- G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
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Abstract
The invention discloses a settlement monitoring method based on D-InSAR technology and image weighted superposition, which comprises the following steps: selecting a plurality of public main images from an SAR image database, carrying out baseline estimation on the processed SAR images to generate a connection image, processing the corrected SAR images to generate a plurality of interference images, carrying out filtering processing on all the interference images and calculating the coherence of the interference images, carrying out phase unwrapping on each interference image after filtering processing, removing the interference images with poor coherence from the unwrapped interference images, carrying out orbit refining and re-flattening processing on the removed interference images, carrying out weighted superposition on the interference images, and finally carrying out phase transformation processing on the phase images and generating a deformation image after geocoding. The invention better combines the advantages of the D-InSAR technology and the weighted superposition, can improve the accuracy of monitoring the ground settlement, and reduce the requirement of data volume, thereby being capable of monitoring the time sequence deformation of the region in a large range for a long time.
Description
Technical Field
The invention belongs to the field of synthetic aperture radar interferometry, and particularly relates to a ground settlement monitoring method of a time sequence D-InSAR technology.
Background
Ground subsidence is a local engineering geological phenomenon which causes the elevation of the crust surface to slowly change under the comprehensive influence of natural change and human activities. With the increasing progress of global urbanization, the expansion of cities and the rapid increase of population, the massive construction of urban infrastructure and the over-exploitation of underground resources result in increasingly serious urban ground subsidence. Ground settlement can cause serious damage to buildings and production facilities, is not beneficial to urban construction and resource exploration and development, and even can cause seawater backflow in coastal areas, thereby causing great potential safety hazards to the safety of lives and properties of people and the safety of infrastructure facilities. Although the conventional monitoring methods such as Global Positioning System (GPS) and leveling are high in accuracy, the conventional monitoring methods can only perform measurement at a small range of points and lines, are difficult to monitor a large range of areas, and are difficult to maintain for a long time.
In a traditional Interferogram Stacking method (interferrogram Stacking), a ground deformation quantity is regarded as a linear change, phase errors of atmospheric disturbance contained in interferograms which are independent of each other are approximated to a random quantity in time, surface deformation information of a research area is regarded as a linear change, and the signal-to-noise ratio between the deformation information and atmospheric interference noise in a stacked phase graph is improved through linear Stacking. The method can realize deformation monitoring for a long time under the support of only a small amount of SAR data. However, if the quality weight of the interferogram is not taken into consideration, the interferogram with low coherence is added to the overlay, which inevitably affects the accuracy of the monitoring result.
Disclosure of Invention
The invention provides a settlement monitoring method based on a D-InSAR technology and image weighted superposition to solve the defects of the existing method, so that high monitoring precision can be achieved by using fewer images, and large-scale time sequence deformation monitoring can be performed.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a settlement monitoring method based on D-InSAR technology and image weighted superposition, which is characterized by comprising the following steps:
step 1, selecting a plurality of public main images from an SAR image database by a public main image selection method;
step 1.1, carrying out SAR data preprocessing on each SAR image in an SAR image database to obtain a corrected SAR image;
step 1.2, calculating the average value d of the distance regularization of the first image by using the formula (1)l：
In formula (1), l represents the serial number of the SAR image in the SAR image database; b isi,lRepresents the vertical spatial baseline, B, between the ith and the lth corrected SAR images during the regularized distance computation of image lcA threshold value representing a vertical spatial baseline; t isi,lRepresenting a time baseline, T, between the ith corrected SAR image and the first corrected SAR image in the regularization distance calculation process of the first corrected SAR imagecA threshold value representing a time baseline; f. ofi,lShowing the Doppler centroid frequency difference between the ith corrected SAR image and the first corrected SAR image in the regularization distance calculation process of the first corrected SAR image, fcA threshold value representing a difference in doppler centroid frequency; n represents the number of interference image pairs generated by the first image in the process of generating the interference image;
step 1.3, selecting an SAR image corresponding to the minimum average value of the previous m frames from an SAR image database as a public main image;
step 2, performing baseline estimation on the M public main images and the rest M-M corrected SAR images respectively to generate a connection diagram; wherein M is the number of SAR images in an SAR image database;
step 3, processing the corrected SAR image according to the digital elevation model DEM, orbit data corresponding to the SAR image and a connection diagram and generating a plurality of interferograms;
step 4, filtering all interferograms and calculating the coherence of the interferograms;
step 5, performing phase unwrapping on each interference pattern after filtering processing to obtain an unwrapped interference pattern, and then removing interference patterns with poor coherence from the unwrapped interference pattern to obtain a removed interference pattern; performing track refining and re-flattening treatment on the interference pattern after the interference pattern is removed to obtain a re-flattened interference pattern; calculating the number of coherent points in the interference image after re-flattening, and selecting the interference image with the highest number of coherent points as a reference interference image;
step 6, weighted superposition of the interferograms;
step 6.1, when the number of coherent points in the interference image after the re-flattening is less than half of the number of coherent points in the reference interference image, setting the weight of the interference image after the re-flattening to be 0, otherwise, setting the weight of the interference image after the re-flattening to be 1;
step 6.2, superposing all the interference patterns subjected to the re-flattening according to the corresponding weights thereof so as to obtain superposed phase patterns;
and 7, carrying out phase transformation processing on the phase diagram, and generating a deformation diagram after geocoding for monitoring settlement.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, a plurality of main images are selected, deformation monitoring is carried out on the ground surface by adding the image quality weight on the basis of the traditional Stacking method, and the monitoring precision is improved compared with that of the traditional monitoring method.
2. The method can better combine the advantages of the D-InSAR technology and the weighted superposition, simplifies the implementation steps, improves the precision of monitoring the ground settlement, reduces the requirement of data volume, and can monitor the time sequence deformation of the region in a large range for a long time.
3. The invention considers the comprehensive influence of the vertical space baseline, the time baseline and the Doppler centroid frequency difference, and utilizes the comprehensive function model to select a plurality of main images, thereby weakening the atmospheric delay influence of the main images and improving the monitoring precision.
4. The method performs weighting on the quality (namely the number of high coherence points) of the coherence map, solves the negative influence of a low coherence map on a monitoring result, and obviously improves the signal-to-noise ratio between deformation information of the phase map and atmospheric noise after weighted superposition.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a graph showing the results of the experiment according to the present invention;
FIG. 3 is a graph of experimental verification accuracy of the present invention.
Detailed Description
In this embodiment, a settlement monitoring method based on a D-InSAR technique and image weighted superposition is to select a plurality of superior main images, register the remaining images with the main images, screen out interference pairs with shorter temporal-spatial baselines to generate different sets for interference pairs generated by registering to the same main image, the baselines between the sets are longer, obtain ground deformation information of each set by a least square method, and solve each set after combining by using a singular decomposition method. The method can obtain higher monitoring precision on the basis of less data sets. Specifically, as shown in fig. 1, the method includes the following steps:
step 1, selecting a plurality of public main images from an SAR image database by a public main image selection method;
step 1.1, carrying out SAR data preprocessing on 9 SAR images in an SAR image database to obtain a corrected SAR image;
step 1.2, calculating the average value d of the distance regularization of the first image by using the formula (1)l：
In formula (1), l represents the serial number of the SAR image in the SAR image database; b isi,lRepresents the vertical spatial baseline, B, between the ith and the lth corrected SAR images during the regularized distance computation of image lcA threshold value representing a vertical spatial baseline; t isi,lRepresenting a time baseline, T, between the ith corrected SAR image and the first corrected SAR image in the regularization distance calculation process of the first corrected SAR imagecA threshold value representing a time baseline; f. ofi,lShowing the Doppler centroid frequency difference between the ith corrected SAR image and the first corrected SAR image in the regularization distance calculation process of the first corrected SAR image, fcA threshold value representing a difference in doppler centroid frequency; n represents the number of interference image pairs generated by the first image in the process of generating the interference image;
step 1.3, calculating a distance regularization average value of 9 SAR images, and selecting the SAR image corresponding to the minimum average value of the first 3 SAR images as a public main image;
step 2, performing baseline estimation on the 3 public main images and the other 6 corrected SAR images respectively to generate a connection diagram;
and 3, eliminating satellite orbit residual phase errors according to orbit data corresponding to the DEM and SAR images of the digital elevation model, and then selecting 11 interference pairs smaller than 50m in all spatial vertical baselines according to the estimated value of the baselines to perform interference processing to generate an interferogram so as to inhibit phase errors caused by spatial decorrelation.
Step 4, performing Goldstein filtering processing on all generated interferograms and calculating the coherence of the interferograms;
step 5, performing phase unwrapping on each interference pattern after filtering processing to obtain an unwrapped interference pattern, and then removing interference patterns with poor coherence from the unwrapped interference pattern to obtain a removed interference pattern; performing track refining and re-flattening treatment on the interference pattern after the interference pattern is removed to obtain a re-flattened interference pattern; calculating the number of coherent points in the interference image after re-flattening, and selecting the interference image with the highest number of coherent points as a reference interference image;
step 6, weighted superposition of the interferograms;
step 6.1, when the number of coherent points in the interference image after the re-flattening is less than half of the number of coherent points in the reference interference image, setting the weight of the interference image after the re-flattening to be 0, otherwise, setting the weight of the interference image after the re-flattening to be 1;
step 6.2, superposing all the interference patterns subjected to the re-flattening according to the corresponding weights thereof so as to obtain superposed phase patterns;
and 7, performing phase transformation processing on the phase diagram, and generating a deformation diagram after geocoding, wherein the deformation diagram can be used for monitoring settlement as shown in FIG. 2.
And (3) analyzing an experimental result:
considering the superiority of monitoring deformation by a multiple main image interferogram weighted superposition method, selecting an urban area of the san Diego county as a test area, and avoiding an area with low coherence so as to improve monitoring precision. Experiments were performed using slc (single Looking complex) data for the 9-scene IW mode of Sentinel-1A from 7 months in 2019 to 12 months in 2019.
To verify the effectiveness of the method, it is necessary to calculate the weight of each image according to the theory of setting weights in the foregoing, and set the weight with low coherence as 0. And superposing the 11 phase unwrapping graphs meeting the requirements, and calculating to obtain the average sedimentation rate shown in the figure 2. As can be seen from fig. 2, during the monitoring process, the surface of the area is in a stable state as a whole, which is related to the short time interval of the selected SAR images. The elevation is larger in the central area of the san Diego county, the annual average ground elevation rate is about 10mm a < -1 >, and the maximum elevation rate of a local area can reach 30mm a < -1 >. The settlement is caused in different degrees in the northern part and the peripheral area of the san Diego county, the annual average ground settlement rate of most areas is-16 mm & alpha & lt-1 & gt, and the main reason for the settlement is that continuous agricultural irrigation and urban construction water in the area are sourced from aquifer water storage, so that underground water is excessively exploited, the underground water level is reduced, and the aquifer is overdrawn and difficult to recover, thereby further worsening the ground settlement. Later, due to a series of local policy protection for groundwater, the deformation of urban areas generally remains stable, and the ground surface deformation rate has a significantly reduced characteristic compared with the historical monitoring result, which indicates that the ground subsidence of the areas is already relieved.
In this embodiment, an analysis module in the SPSS software is used to analyze the relationship between the groundwater level and the ground settlement, and the result is shown in fig. 3. And establishing data of the relationship between the deep underground water level and the ground settlement amount on the basis of the underground water monitoring points, and analyzing to obtain a table 1.
TABLE 1 correlation analysis of ground water level and ground subsidence
The correlation coefficient of the groundwater level and the ground settlement is 0.941 obtained by analyzing in table 1, and the correlation degree between the groundwater level and the ground settlement is very high.
In conclusion, the method provided by the invention makes up the defect that the D-InSAR technology cannot carry out time sequence monitoring, solves the problem that the time sequence InSAR technology has more requirements on data volume, reduces the adverse effect caused by the participation and superposition of low-coherence images in the traditional Stacking method, improves the monitoring precision, is effective and feasible, and can be practically applied as a high-efficiency and high-precision ground settlement monitoring method.
Claims (1)
1. A settlement monitoring method based on D-InSAR technology and image weighted superposition is characterized by comprising the following steps:
step 1, selecting a plurality of public main images from an SAR image database by a public main image selection method;
step 1.1, carrying out SAR data preprocessing on each SAR image in an SAR image database to obtain a corrected SAR image;
step 1.2, calculating the average value d of the distance regularization of the first image by using the formula (1)l：
In formula (1), l represents the serial number of the SAR image in the SAR image database; b isi,lRepresents the vertical spatial baseline, B, between the ith and the lth corrected SAR images during the regularized distance computation of image lcA threshold value representing a vertical spatial baseline; t isi,lShowing the SAR image after the first correctionTime base line, T between ith corrected SAR image and ith corrected SAR image in image regularization distance calculation processcA threshold value representing a time baseline; f. ofi,lShowing the Doppler centroid frequency difference between the ith corrected SAR image and the first corrected SAR image in the regularization distance calculation process of the first corrected SAR image, fcA threshold value representing a difference in doppler centroid frequency; n represents the number of interference image pairs generated by the first image in the process of generating the interference image;
step 1.3, selecting an SAR image corresponding to the minimum average value of the previous m frames from an SAR image database as a public main image;
step 2, performing baseline estimation on the M public main images and the rest M-M corrected SAR images respectively to generate a connection diagram; wherein M is the number of SAR images in an SAR image database;
step 3, processing the corrected SAR image according to the digital elevation model DEM, orbit data corresponding to the SAR image and a connection diagram and generating a plurality of interferograms;
step 4, filtering all interferograms and calculating the coherence of the interferograms;
step 5, performing phase unwrapping on each interference pattern after filtering processing to obtain an unwrapped interference pattern, and then removing interference patterns with poor coherence from the unwrapped interference pattern to obtain a removed interference pattern; performing track refining and re-flattening treatment on the interference pattern after the interference pattern is removed to obtain a re-flattened interference pattern; calculating the number of coherent points in the interference image after re-flattening, and selecting the interference image with the highest number of coherent points as a reference interference image;
step 6, weighted superposition of the interferograms;
step 6.1, when the number of coherent points in the interference image after the re-flattening is less than half of the number of coherent points in the reference interference image, setting the weight of the interference image after the re-flattening to be 0, otherwise, setting the weight of the interference image after the re-flattening to be 1;
step 6.2, superposing all the interference patterns subjected to the re-flattening according to the corresponding weights thereof so as to obtain superposed phase patterns;
and 7, carrying out phase transformation processing on the phase diagram, and generating a deformation diagram after geocoding for monitoring settlement.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109029344A (en) * | 2018-07-10 | 2018-12-18 | 湖南中科星图信息技术有限公司 | A kind of dykes and dams Monitoring method of the subsidence based on high score image and lift rail InSAR |
| CN108957456A (en) * | 2018-08-13 | 2018-12-07 | 伟志股份公司 | Landslide monitoring and EARLY RECOGNITION method based on multi-data source SBAS technology |
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| 罗海滨，何秀凤: "PS2DInSAR 时序差分干涉图公共主影像选取方法", 《河海大学学报（自然科学版）》 * |
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