CN110187346A - A kind of ground SAR Detection of Gross Errors method under complex working condition - Google Patents
A kind of ground SAR Detection of Gross Errors method under complex working condition Download PDFInfo
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- CN110187346A CN110187346A CN201910540382.XA CN201910540382A CN110187346A CN 110187346 A CN110187346 A CN 110187346A CN 201910540382 A CN201910540382 A CN 201910540382A CN 110187346 A CN110187346 A CN 110187346A
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Abstract
The present invention provides a kind of ground SAR Detection of Gross Errors methods under complex working condition, it is by introducing the signal Singularity Detection based on wavelet analysis, image identification can be made to block and be converted into Detection of Gross Errors problem, position the image blocked by carrying out Singularity Detection to the timing rhohase for surveying area PS point.The present invention can solve GBSAR, and image that may be present blocks the problem of brought measurement data contains rough error in practical engineering applications, the efficiency and accuracy of monitoring section puppet Image detection are improved, is provided fundamental basis for engineer application Data processing of the ground SAR under complex environment in the future.
Description
Technical field
The present invention relates to a kind of GBSAR image detection method, in particular to ground SAR Detection of Gross Errors under a kind of complex working condition
Method.
Background technique
Ground synthetic aperture radar (GBSAR) is a kind of novel surface microwave remote sensing technology for deformation monitoring, has been combined dry
Relate to measuring technique, CW with frequency modulation technology and synthetic aperture radar technique, the deformation of achievable contactless regional large area
Monitoring has the advantages that round-the-clock, round-the-clock, continuous monitoring, sample frequency are high, and monitoring accuracy can reach millimeter even submillimeter amount
Grade.The situation and trend that can obtain monitored target change in displacement in real time provide accurate warning information before disaster occurs, this
A little features make it have very wide application prospect in deformation monitoring field.
GBSAR has very high sensitivity, it is easy to be interfered by error, the Interferometric phase error in data acquisition is it
Most important error source.It may be subjected to the shadow that external environment and human factor block radar line of sight in the rotating detection process of field
It rings, so that singular value occurs in interferometric phase, the image that will be blocked is needed to detect.
Summary of the invention
For solve GBSAR in practical engineering applications image that may be present block brought by measurement data contain rough error
The case where, the present invention provides a kind of GBSAR Detection of Gross Errors methods under complex working condition, by introducing the letter based on wavelet analysis
Number Singularity Detection theory, can make to block image identification and be converted into Detection of Gross Errors problem, pass through to the timing rhohase for surveying area PS point
Singularity Detection is carried out to position the image kept and blocked；The method increase the efficiency of Image detection and accuracys, are ground SAR
Engineer application Data processing under complex environment in the future is provided fundamental basis.
In order to achieve the above objectives, the technical solution of the present invention is as follows: a kind of ground SAR Detection of Gross Errors method under complex working condition,
The following steps are included:
1) it is focused by GBSAR acquisition original amplitude and phase data and is handled with differential interferometry, and carried out atmospheric correction and obtain
Survey the overall preliminary deformation calculation result in area；
2) judge whether data contain according to the preliminary deformation calculation result of whole two dimension and PS point deformation sequence combination actual conditions
Rough error；
If 3) contain rough error, extracts and survey area PS point phase sequence；
4) suitable wavelet basis function is chosen according to PS point phase temporal aspect, and determine wavelet-order to PS point phase sequence into
Row Singularity Detection；
PS point phase sequenceWavelet transformation formal definition are as follows:
In formula,For displacement；It is integration variable；For wavelet conversion coefficient；It is wavelet basis function.Work as scaleVery little
When, isolated point takes maximum in wavelet transformation, can find phase singularity by wavelet modulus maxima point.If wavelet basis
FunctionIt is smooth functionFirst derivative, then corresponding phase sequenceWavelet transformation are as follows:
It is found thatWithBySmoothed out derived function is directly proportional.To a certain particular dimensions,Edge
Catastrophe point of the maximum of time shaft corresponding to timing rhohase after smoothed, thenExtreme point position should appear in phase
Near the modulus maximum point of bit sequence.
5) union for taking PS point phase sequence singular value detection result obtains the image set D1(N width containing rough error)；
6) to avoid the burst deformation in the short time being accidentally classified as rough error, by image set D1, temporally span is divided into K group, in each group
It carries out mutually interference to obtain N (N-K)/2 width interference pattern and count residue points, takes three of each width interference pattern residual error points mean value in every group
Error is threshold value in times, by residue points differenceImage be classified as normal images, obtain being blocked image
Collect D2；
7) it is accidentally classified as rough error to avoid the substantially depression that there will be trend early period and sliding caused deformation, takes each shadow in D2 set
Picture and its each L width image in front and back (L size is fixed according to sample frequency) carry out the processing such as interference and atmospheric correction, to gained PS point phase
Timing curve carries out least square fitting, and the image is then classified as normal images by linear trend if it exists, is obtained slightly through this process
Poor image set D3；
8) rough error image set D3 rejecting will be contained, obtain surveying the true deformation monitoring image set D in area.
The technical effects of the invention are that: present invention efficiently solves the acquisitions of GBSAR data by external environmental interference band
The rough error come influences, and effectively remains the characteristic trend of data script；The present invention converts quality of image problem analysis to
Detection of Gross Errors problem can more effectively reject image containing rough error；The application that the present invention is GBSAR under complex working condition provides reason
By and using foundation, the application conditions of GBSAR technology have been widened to a certain extent.
Detailed description of the invention
Fig. 1 is the PS deformation inversion algorithm flow chart that GBSAR of the present invention is handled in real time.
Specific embodiment
The present invention will be further described below with reference to the drawings.It should be pointed out that following embodiments are of the invention
The specific implementation method of optimization, the scope of protection of the present invention is not limited thereto.
A kind of ground SAR Detection of Gross Errors method under complex working condition, specific method and steps are as follows:
As shown in Figure 1, in deformation inversion stage, by raw video to interference pattern is generated, according to the knot of certain rule PS point selection
Fruit extracts the interference complex data of PS point in interference pattern, twines phase by the preliminary solution for carrying out phase unwrapping acquisition PS point to PS point
Position, then estimate and compensate atmospheric phase, according to the actual situation to heavy rail error phase estimation compensation, by turn of phase and deformation quantity
The relationship of changing extracts the deformation data of all PS points.By Delaunay triangulation network linear interpolation method, entire scene areas is extracted
Relative deformation information.In view of original deformation quantity inversion result is there may be the influence of noise, the side filtered by time dimension
Method inhibits random noise, obtains the preliminary deformation calculation result of point target.
Whether data are judged according to the deformation inversion result and survey area's actual conditions of preliminary deformation calculation result and point target
Containing rough error, the extraction for surveying area PS timing rhohase is carried out if containing rough error；It is suitable according to being chosen the characteristics of PS point timing rhohase
Wavelet basis function, and determine wavelet basis function order, to PS point timing rhohase carry out Singularity Detection；Take PS point phase sequence
The union of column singular value detection result obtains the image set D1(N width containing rough error)；To avoid monitoring region from there is burst deformation
Or collapse situation and be accidentally classified as the phenomenon that blocking equal rough errors, increase the feature judgement of mutation front and back deformation tendency, first by image set
Temporally span is divided into K group to D1, mutual coherent interference processing is carried out in each group, N (N-K)/2 width interference pattern is obtained, and utilized
Matlab software carries out residue points statistics to each width interference pattern based on interference pattern residue points Computing Principle, takes interference pattern in every group
Error is threshold value in the three times of residual error points mean value, by residue points differenceImage be classified as normal images,
It obtains being blocked image set D2；Then take D2 gather in each L width image of each image and its front and back (L size according to sample frequency fixed) into
The processing such as row interference and atmospheric correction, carries out least square fitting to gained PS point phase timing curve, if it exists linear trend
The image is then classified as normal images, obtains rough error set D3 through this process；Finally, rough error image set D3 rejecting will be contained,
Obtain the image set D that actual response surveys area's deformation.
Claims (1)
1. a kind of ground SAR Detection of Gross Errors method under complex working condition, which comprises the following steps:
1) it is focused by GBSAR acquisition original amplitude and phase data and is handled with differential interferometry, and carried out atmospheric correction and obtain
Survey the overall preliminary deformation calculation result in area；
2) judge whether data contain rough error according to preliminary deformation calculation result and PS point deformation characteristic sequence；
If 3) contain rough error, extracts and survey area PS point phase sequence；
4) suitable wavelet basis function is chosen according to PS point phase temporal aspect, and determine wavelet-order to PS point phase sequence into
Row Singularity Detection；
5) union for taking PS point phase singularity value detection result, obtains the image set D1 containing rough error；
6) by image set D1, temporally span is divided into K group, carries out image in each group and interferes to obtain N (N-K)/2 width interference pattern
And residue points are counted, taking error in the three times of each width image interference figure residual error points mean value is threshold value, by residue points differenceImage be classified as normal images, and then obtain being blocked image set D2；
7) each L width image of each image and its front and back in image set D2 is taken to carry out interference and atmospheric correction processing, to gained PS point phase
Bit timing curve carries out least square fitting, and the image is then classified as normal images by linear trend if it exists, is obtained through this process
Rough error image set D3；
8) rough error image set D3 rejecting will be contained, obtain surveying the true deformation monitoring image set D in area.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116642440A (en) * | 2023-05-05 | 2023-08-25 | 首都师范大学 | Small baseline set interferometry method based on wavelet filtering |
Citations (3)
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| ES2355340A1 (en) * | 2010-07-26 | 2011-03-25 | Consorci Institut De Geomatica | A method for monitoring terrain and man-made feature displacements using ground-based synthetic aperture radar (GBSAR) data |
| CN106772342A (en) * | 2017-01-11 | 2017-05-31 | 西南石油大学 | A kind of Timing Difference radar interference method suitable for big gradient surface subsidence monitoring |
| 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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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ES2355340A1 (en) * | 2010-07-26 | 2011-03-25 | Consorci Institut De Geomatica | A method for monitoring terrain and man-made feature displacements using ground-based synthetic aperture radar (GBSAR) data |
| CN106772342A (en) * | 2017-01-11 | 2017-05-31 | 西南石油大学 | A kind of Timing Difference radar interference method suitable for big gradient surface subsidence monitoring |
| 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 |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116642440A (en) * | 2023-05-05 | 2023-08-25 | 首都师范大学 | Small baseline set interferometry method based on wavelet filtering |
| CN116642440B (en) * | 2023-05-05 | 2024-07-23 | 首都师范大学 | Small baseline set interferometry method based on wavelet filtering |
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