CN105954748B - TS-InSAR atmospheric phase filtering methods based on partition strategy - Google Patents
TS-InSAR atmospheric phase filtering methods based on partition strategy Download PDFInfo
- Publication number
- CN105954748B CN105954748B CN201610265353.3A CN201610265353A CN105954748B CN 105954748 B CN105954748 B CN 105954748B CN 201610265353 A CN201610265353 A CN 201610265353A CN 105954748 B CN105954748 B CN 105954748B
- Authority
- CN
- China
- Prior art keywords
- data block
- scattering point
- value
- filter
- permanent scattering
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Image Processing (AREA)
Abstract
The present invention provides a kind of TS InSAR atmospheric phase filtering methods based on partition strategy, including：Original SAR image data is divided into data block and counts permanent scattering point information；The Selection Center data chunk centered on data block, filtering parameter and permanent scattering point information based on setting calculate the center filter value of centre data block group and Neighborhood Filtering value, and the filter value of permanent scattering point is worth to based on center filter value and field filtering.Each permanent scattering point that do not need to of the present invention computes repeatedly filter value, substantially increases filtration efficiency；Accurate filtering method is combined with approximate filtering method, can ensure very high precision while significant increase speed；Different filter radius and different data types can be adapted to very well.
Description
Technical field
The present invention relates to interference synthetic aperture radar technical fields, are a kind of big gas phases of the TS-InSAR based on partition strategy
Position filtering method.
Background technology
Synthetic Aperture Radar Technique is monitored applied to Ground Deformation, the theory proposed earliest is heavy rail differential interferometry skill
Art, however time and the influence of space decoherence and different moments atmospheric oscillation are limited by, heavy rail differential interferometry technology is used for
Ground Deformation monitoring is very limited, and makes to synthesize hole using the interference of traditional heavy rail differential technique just because of these limitations
Diameter radar (InSAR) is extremely difficult to ideal meter level digital elevation model (DEM) and grade deformation monitoring, in order to solve these
Problem, time series interference technique (TS-InSAR) come into being, and apply the stronger Coherent Targets of coherence of limited quantity,
Namely Permanent scatterers (permanent scatterer, PS), these PS points form the corner reflector net of one " natural ", it can
Efficiently against time and space decorrelation and the influence of atmospheric phase, really to obtain meter level digital elevation model (DEM)
With grade Ground Deformation data.Simultaneously as PS points are not influenced by time and space decorrelation so that SAR image data
The limitation of original time and Space Baseline is breached, available SAR images quantity greatly increases, and is greatly improved data
Utilization rate also provides condition for the integrated of different SAR image datas.And a critical issue of time series interference technique is just
It is to be extracted from residual phase due to phase caused by atmospheric oscillation, so as to remove this atmospheric phase in differential phase,
Obtain high-precision non-linear deformation phase.
The general fashion for obtaining atmospheric phase from remaining difference interferometric phase at present is phase filtering technology.Utilize air
The time domain height of phase is uncorrelated, i.e. high frequency characteristics, spatial domain height auto-correlation, i.e. low frequency characteristic.It is empty in time-domain high-pass filtering
Between domain low-pass filtering, obtain atmospheric phase.And for traditional weighted mean airspace filter technology, time-consuming, computationally intensive, with
The promotion of required precision, time complexity will sharply increase, and there is a large amount of redundant operations, and picture size is bigger,
Operation efficiency is lower, these significant drawbacks that can not ignore seriously restrict its application.Such as there is 56 width images, figure for one
Be 400 (distance to) × 1500 (orientations) as size is smaller, for the time series with 14190 PS points, tradition regardless of
The filtering time of block method is 20s or so.And in practice image be as unit of scape, orientation and distance per scape image to
Sampled point can reach tens thousand of magnitudes, and PS point numbers reach hundreds of thousands, reach hour magnitude even using conventional method filtering time
Several days, forecast for disaster alarm or weather conditions, far can not reach disaster alarm and weather conditions forecast real-time and
Accuracy requirement.
In short, the shortcomings of existing phase filtering technology also existence time complexity is high, and operand is big, and redundant operation is more,
Therefore it is badly in need of developing a kind of for the quick of high-volume large scene SAR image data, high-precision, atmospheric phase filtering technique.
Invention content
(1) technical problems to be solved
In order to solve prior art problem, the present invention provides a kind of TS-InSAR atmospheric phases filters based on partition strategy
Wave method.
(2) technical solution
The present invention provides a kind of TS-InSAR atmospheric phase filtering methods based on partition strategy, including：Step A：It will
Original SAR image data is divided into data block；Step B：Permanent scattering point information in statistical data block；Step C：With a data
Selection Center data chunk centered on block, filtering parameter and permanent scattering point information based on setting, calculates the centre data
The center filter value of block group；Step D：Filtering parameter and permanent scattering point information based on setting, calculate using the data block as
The Neighborhood Filtering value of the centre data block group at center；Step E：Step C, D is performed, and by to data blocks all in SAR images
The filter value array of the centre data block group centered on each data block is arrived in heart filter value and the storage of Neighborhood Filtering value；And step
Rapid F：Based on the filter value array of the centre data block group centered on the data block where permanent scattering point, by the filter value array
Center filter value and field filter value, obtain the filter value of permanent scattering point.
(3) advantageous effect
It can be seen from the above technical proposal that the TS-InSAR atmospheric phase filtering methods based on partition strategy of the present invention
It has the advantages that：
(1) speed is fast；
Relative to traditional filtering method, this method utilizes partition strategy, by the center filter value and Neighborhood Filtering of data block
Value is calculated before filtering, and when filtering only needs to add up the two, does not need to each permanent scattering point and computes repeatedly
Filter value, substantially increases filtration efficiency, and speed promotes nearly 10 times；
(2) precision is high；
The present invention is for the centre data block group in filter window, using accurate filtering method, in order to promote speed, needle
To the corresponding adjacent region data block of centre data block group using approximate filtering method, since window center weight is larger, edge weights
It is smaller, therefore adjacent region data block can be ignored since error caused by approximation is very small, therefore the present invention is in significant increase speed
It can ensure very high precision simultaneously；
(3) robustness is good；
It can be well adapted to for different filter radius, when filter radius is less than 20 (distance to), the present invention can be with
It is adaptive to reduce block size, ensure filtering accuracy, for different data types (real number, plural number), the present invention still may be used
To adapt to very well.
Description of the drawings
Fig. 1 is the flow chart of the TS-InSAR atmospheric phase filtering methods based on partition strategy of the embodiment of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference
Attached drawing, the present invention is described in more detail.It should be noted that in attached drawing or specification description, similar or identical portion
Divide and all use identical figure number.The realization method for not being painted or describing in attached drawing is those of ordinary skill in technical field
Known form.In addition, though the demonstration of the parameter comprising particular value can be provided herein, it is to be understood that parameter is without definite etc.
In corresponding value, but can be similar to be worth accordingly in acceptable error margin or design constraint.It is mentioned in embodiment
Direction term, such as " on ", " under ", "front", "rear", "left", "right" etc. are only the directions of refer to the attached drawing.Therefore, the side used
Protection scope of the present invention is intended to be illustrative and not intended to limit to term.
An embodiment of the present invention provides a kind of TS-InSAR atmospheric phase filtering methods based on partition strategy, specific to wrap
It includes：
Step A：Original SAR image data is divided into data block.Using partition strategy, by the center filter value of data block
It is calculated before filtering with Neighborhood Filtering value, subsequently only needs to add up the two during filtering, do not need to be each permanent scattered
Exit point all computes repeatedly filter value, can greatly improve filtration efficiency, and experiment shows that speed can promote nearly 10 times.
Step A is specifically included：For each width image data in several original SAR image datas, by the width image number
The data block identical according to size is divided into.
To several original SAR image datas, first according to the size of original SAR images, by every original SAR image data
It is divided into the identical data block of several sizes namely uniform grid division is carried out to image, each grid corresponds to a data block, and
Each data block is numbered, the sequence of number is by left-to-right, from top to bottom.
Wherein, the identical data block of size refers to distance to points and the data block of orientation points all same.In order to protect
Demonstrate,prove enough filtering accuracies, the robustness of Enhancement Method, the size of data block is directly proportional to the filter radius subsequently chosen；It is preferred that
Ground, the size of data block are distance to 7 × orientation 35 or distance to 6 × orientation 30.
Step B：Permanent scattering point information in statistical data block.
Step B is specifically included：One data block is traversed, identify permanent scattering point and counts the permanent scattered of the data block
Thus exit point information obtains the permanent scattering point information of all data blocks of all original SAR image datas.
Preferably, the point that traversal refers to the orientation of ergodic data block line by line is carried out to data block；Or number is traversed by column
According to block distance to point.
Preferably, the permanent scattering point information of the data block includes the quantity, each of the permanent scattering point in the data block
The last phase place value and location information of permanent scattering point.
Preferably, it is permanently dissipated by coherence factor threshold method, amplitude threshold method or phase deviation threshold method identification data block
Exit point.
Step C：The Selection Center data chunk centered on a data block, filtering parameter and permanent scattering point based on setting
Information calculates the center filter value of centre data block group.
Step C is specifically included：
Sub-step C1：A data block organization center data chunks of (2X-1) × (2X-1) centered on the data block, institute
State X >=2, preferably 2 or 3, wherein, block centered on the data block, other data blocks of centre data block group in addition to central block
For periphery block.
Sub-step C2：Using the permanent scattering point in central block as the filter center for the filter window that filter radius is R.
Wherein, filter radius R is more than data block distance at least one of points and orientation points so that filtering
Window covers multiple data blocks.
Sub-step C3：Calculate the weighted value of all permanent scattering points of central block and periphery block.
Wherein, the weighted value of permanent scattering point is related with its location information, i.e., the distance of permanent scattering point and filter center
It is inversely proportional with the weighted value of permanent scattering point.The weighted value of k-th of permanent scattering point is in central block
Wherein, rkCentered on the distance value of k-th of permanent scattering point and filter center in block；J-th of permanent scattering in i-th of periphery block
Point weighted value beWherein, rI, jFor in j-th of permanent scattering point and filtering in i-th of periphery block
The distance value of the heart；The weighted value of the permanent scattering point as filter center is 1.
Sub-step C4：The weighted value of all permanent scattering points and last phase place value based on central block and periphery block calculate
The centre data block group filter value of the permanent scattering point.
Wherein, the central block group filter value of the permanent scattering point
Wherein, centered on K the permanent scattering point of block quantity；Q (k) andK ∈ [1,2 ..., K], respectively central block is permanent
The last phase place value and weighted value of scattering point；I is the quantity of periphery block；JiThe quantity of permanent scattering point for i-th of periphery block；
Q (i, j) andI ∈ [1,2 ..., I], j ∈ [1,2 ..., Ji], the respectively last phase of the permanent scattering point of periphery block
Place value and weighted value.
Sub-step C5：Sub-step C2, C3 and C4 are performed to each permanent scattering point in central block, obtained in the central block
All permanent scattering points centre data block group filter value, centered on data chunk center filter value, and store into
In the filter value array of heart data chunk.
Wherein, the filter value array of the centre data block group is F (l, k), is counted centered on l ∈ [1,2 ..., L], wherein l
According to the number of block group, which takes the number of the central block of the centre data block group, the data block that L is included per scape SAR images
Sum；The quantity of the permanent scattering point of block centered on k ∈ [1,2 ..., K], K, after central block determines, the filter value array packet
Containing K filter value.
The centre data block group that aforesaid way utilizes the centre data block group for including periphery block to calculate permanent scattering point filters
Value, filtering accuracy are high.
In other embodiments, the X takes 1, and the centre data block group only includes the data block, not comprising periphery block,
The weighted value of all permanent scattering points of central block is only calculated, and based on the weighted value and last phase of all permanent scattering points of central block
Place value calculates the centre data block group filter value of permanent scattering point, and the centre data block group filter value of permanent scattering point is deposited
Storage is in the filter value array of centre data block group, in this way while certain filtering accuracy is ensured, reduce algorithm complexity,
Improve arithmetic speed.
It is specific as follows：
Sub-step C2：Using the permanent scattering point in central block as the filter center for the filter window that filter radius is R.
Wherein, filter radius R is more than data block distance at least one of points and orientation points so that filtering
Window covers multiple data blocks.
Sub-step C3：Calculate the weighted value of all permanent scattering points of central block.
Wherein, the weighted value of permanent scattering point is related with its location information, i.e., the distance of permanent scattering point and filter center
It is inversely proportional with the weighted value of permanent scattering point, the weighted value of k-th of permanent scattering point is in central block
Wherein, rkCentered on the distance value of k-th of permanent scattering point and filter center in block, the permanent scattering point as filter center
Weighted value be 1.
Sub-step C4：It is permanent scattered to calculate this for the weighted value of all permanent scattering points and last phase place value based on central block
The centre data block group filter value of exit point.
Wherein, the central block filter value of the permanent scattering pointWherein, centered on K block it is permanent
The quantity of scattering point；Q (k) andK ∈ [1,2 ..., K], respectively the last phase place value of the permanent scattering point of central block and
Weighted value.
Sub-step C5：Sub-step C2, C3 and C4 are performed to each permanent scattering point in central block, obtained in the central block
All permanent scattering points centre data block group filter value, centered on data chunk center filter value, and store into
In the filter value array of heart data chunk.
Wherein, the filter value array of the centre data block group is F (l, k), block centered on l ∈ [1,2 ..., L], wherein l
Number, the data block total number that L is included per scape SAR images；The number of the permanent scattering point of block centered on k ∈ [1,2 ..., K], K
Amount, after central block determines, which includes K filter value.
Step D：Filtering parameter and permanent scattering point information based on setting, calculate the middle calculation centered on the data block
According to the Neighborhood Filtering value of block group.
Sub-step D1：Using the permanent scattering point in the data block as in the filtering for the filter window that filter radius is R
The heart chooses the adjacent region data block of the centre data block group.
Preferably, the filter radius of filter window is R, and the points of data block orientation are Ra, take and are not less thanMinimum
Integer A, data block distance to points for Rb, take and be not less thanSmallest positive integral B, filter window data block total number for (2 ×
A) × (2 × B), adjacent region data block is removes all data blocks of residue outside centre data block group in filter window.
Sub-step D2：Again using the permanent scattering point in the center of n-th of adjacent region data block as filter center, the data block is chosen
Inside meet the permanent scattering point of standard as the permanent scattering point of target.
Preferably, the permanent scattering point for meeting standard refers to be less than filtering with the distance of filter center in the data block
The permanent scattering point of radius R.
By sub-step D1 and D2 it is found that the permanent scattering point of data block for being in filter window boundary, i.e. part is located at
In filter window, permanently scattering point is located at the data block outside filter window for part, and the present invention can be only using in filter radius
Within permanent scattering point be filtered, i.e., using filter center as reference point, based on filter radius criterion, be accurately determined
Useful permanent scattering point in data boundary block excludes useless permanent scattering point, further improves filtering accuracy.
Sub-step D3：Calculate the weighted value of the permanent scattering point of target, weighted value and remnants based on the permanent scattering point of target
Phase is worth to n-th of Neighborhood Filtering value of the data block.
Wherein, the weighted value of the permanent scattering point of target is related with its location information, i.e., in the permanent scattering point of target and filtering
The distance of the heart and the weighted value of the permanent scattering point of target are inversely proportional.The weighted value of the permanent scattering point of m-th of target isWherein, rM, nFor the filter center of the permanent scattering point of m-th of target and n-th of adjacent region data block
Distance value.
Wherein, n-th of Neighborhood Filtering valueN is adjacent region data block
Quantity；M ∈ [1,2 ..., Mn], MnQuantity for the corresponding permanent scattering point of target of n-th of adjacent region data block；Q (m) is m-th
The last phase place value of the permanent scattering point of target.
Sub-step D4：Sub-step D2 and D3 are performed to all adjacent region data blocks of the centre data block group, obtain the center
The filter value of each adjacent region data block of data chunk, centered on data chunk Neighborhood Filtering value, and be deposited into step C
The filter value array of the centre data block group.
Wherein, which is F (l, K+n), l ∈ [1,2 ..., L], and n ∈ [1,2 ..., N], L are per scape SAR
The data block total number that image is included；N is the sum of adjacent region data block.
Step E：Step C, D is performed, and center filter value and Neighborhood Filtering value are stored to data blocks all in SAR images
To the filter value array of the centre data block group centered on each data block.
Wherein, the filter value array of the centre data block group centered on each data block is F (l, k+n), l ∈ [1,
2 ..., L], k ∈ [1,2 ..., K], the data block total number that n ∈ [1,2 ..., N], wherein L are included for every scape SAR；K is each
The sum of the permanent scattering point of data block, N are the sum of the data block adjacent region data block；The possibility of the K and N of each data block are deposited
In difference.
In step C and D, the present invention is for the centre data block group in filter window, using accurate filtering method, in order to
Promote speed, for the corresponding adjacent region data block of centre data block group using approximate filtering method, due to window center weight compared with
Greatly, edge weights are smaller, therefore adjacent region data block can be ignored since error caused by approximation is very small, therefore the present invention is in pole
It can ensure very high precision while big promotion speed.
Step F：Based on the filter value array of the centre data block group centered on the data block where permanent scattering point, by this
The center filter value of filter value array and field filter value, obtain the filter value of the permanent scattering point.
Wherein, the filter value of permanent scattering pointThe filter value of i.e. permanent scattering point is the permanent scattering point institute
Data block centered on the center filter value of centre data block group and the sum of Neighborhood Filtering value, wherein,Data chunk adjacent region data block sum centered on N, wherein, SnFor the permanent scattering point institute
Data block centered on centre data block group n-th of Neighborhood Filtering value.Meanwhile SnAnd n-th of adjacent region data block is
(l ', K '+n ') a data value of the filter value array F (L, K '+N ') of the centre data block group at center, l ' ∈ [1,2 ...,
L] number for n-th adjacent region data block；N ' is the corresponding central block filter value storage number of the adjacent region data block, at this time center
Block regards the adjacent region data block of the adjacent region data block as；K ' is the sum of permanent scattering point in the adjacent region data block；N ' is the field
The corresponding FIELD Data block sum of data block.
In order to illustrate further advantages of the present invention, below by taking filter radius R=25 (distance is to sampling number) as an example
Illustrate advantage of the method for the present invention compared to traditional filtering method in speed, 56 of image use registered completion are spaceborne
SAR time series interference images, imaging time are 1992 to 2002, the size of every width picture (distance to) × 1500 for 400
(orientation), block size (distance to) × 30 (orientation) for 6 have from the visible the method for the present invention of following table compared with conventional method bright
Aobvious advantage.
1 the method for the present invention of table and conventional method time comparing result (filter radius 20-50)
As it can be seen from table 1 filtering method provided by the invention can be well adapted to for different filter radius, work as filter
When wave radius is less than 20 (distance to), the present invention can adaptively reduce block size, ensure filtering accuracy.In addition, it is directed to
Different data types (real number, plural number), the present invention can still adapt to very well.
So far, attached drawing is had been combined the present embodiment is described in detail.According to above description, those skilled in the art
There should be clear understanding to the TS-InSAR atmospheric phase filtering methods based on partition strategy of the present invention.
It should be noted that in attached drawing or specification text, the realization method that is not painted or describes is affiliated technology
Form known to a person of ordinary skill in the art in field, is not described in detail.In addition, above-mentioned definition to each element and not only limiting
The various modes mentioned in embodiment, those of ordinary skill in the art simply can be changed or replaced to it, such as：
(1) direction term mentioned in embodiment, such as " on ", " under ", "front", "rear", "left", "right" etc. are only ginsengs
The direction of attached drawing is examined, is not used for limiting the scope of the invention；
(2) above-described embodiment can be based on the considerations of design and reliability, and the collocation that is mixed with each other uses or and other embodiment
Mix and match uses, i.e., the technical characteristic in different embodiments can freely form more embodiments.
Particular embodiments described above has carried out the purpose of the present invention, technical solution and advantageous effect further in detail
It describes in detail bright, it should be understood that the above is only a specific embodiment of the present invention, is not intended to restrict the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution, improvement and etc. done should be included in the guarantor of the present invention
Within the scope of shield.
Claims (7)
1. a kind of TS-InSAR atmospheric phase filtering methods based on partition strategy, which is characterized in that including：
Step A：Original SAR image data is divided into data block；
Step B：Permanent scattering point information in statistical data block；
Step C：The Selection Center data chunk centered on a data block, filtering parameter and permanent scattering point letter based on setting
Breath calculates the center filter value of the centre data block group；
Step D：Filtering parameter and permanent scattering point information based on setting, calculate the centre data centered on the data block
The Neighborhood Filtering value of block group；
Step E：All data blocks in SAR images are performed with steps C, D, and by center filter value and the storage of Neighborhood Filtering value arrive with
The filter value array of centre data block group centered on each data block；And
Step F：Based on the filter value array of the centre data block group centered on the data block where permanent scattering point, by the filtering
It is worth the center filter value of array and field filter value, obtains the filter value of permanent scattering point；The step C is specifically included：
Sub-step C1：A data block organization center data chunks of (2X-1) × (2X-1) centered on the data block, the data
Block centered on block, other data blocks of centre data block group in addition to central block are periphery block, and the X is natural number；
Sub-step C2：Using the permanent scattering point in central block as the filter center for the filter window that filter radius is R；
Sub-step C3：Calculate the weighted value of all permanent scattering points of central block and periphery block；
Sub-step C4：The weighted value of all permanent scattering points and last phase place value based on central block and periphery block, calculate this forever
The centre data block group filter value of long scattering point；And
Sub-step C5：Sub-step C2, C3 and C4 are performed to each permanent scattering point in central block, obtain the institute in the central block
There is the centre data block group filter value of permanent scattering point, as the center filter value of the centre data block group, and store to center
In the filter value array of data chunk；
The sub-step C3 is specifically included：The weighted value of permanent scattering point is related with its location information, i.e., permanent scattering point and filter
The distance at wave center and the weighted value of permanent scattering point are inversely proportional, and the weighted value of k-th of permanent scattering point is in central blockWherein, rkCentered on the distance value of k-th of permanent scattering point and filter center in block；I-th of periphery
The weighted value of j-th of permanent scattering point is in blockWherein, ri,jFor j-th in i-th of periphery block forever
The distance value of long scattering point and filter center；The weighted value of the permanent scattering point as filter center is 1；
The sub-step C4 is specifically included：The central block group filter value of the permanent scattering pointWherein, centered on K block permanent scattering point quantity；Q (k) andThe last phase place value and weighted value of the permanent scattering point of k ∈ [1,2 ..., K], respectively central block；I is the number of periphery block
Amount；JiPermanent scattering point quantity for i-th of periphery block；Q (i, j) andi∈[1,2,…,I],j∈[1,2,…,
Ji], the respectively last phase place value and weighted value of the permanent scattering point of periphery block.
2. TS-InSAR atmospheric phases filtering method as described in claim 1, which is characterized in that the step D is specifically included：
Sub-step D1：Using the permanent scattering point in the data block as the filter center for the filter window that filter radius is R, choosing
Take the adjacent region data block of the centre data block group；
Sub-step D2：Again using the permanent scattering point in the center of n-th of adjacent region data block as filter center, choose full in the data block
The permanent scattering point of sufficient standard is as the permanent scattering point of target；
Sub-step D3：Calculate the weighted value of the permanent scattering point of target, weighted value and residual phase based on the permanent scattering point of target
It is worth to n-th of Neighborhood Filtering value of the data block；And
Sub-step D4：Sub-step D2 and D3 are performed to all adjacent region data blocks of the centre data block group, obtain the centre data
The filter value of each adjacent region data block of block group as the Neighborhood Filtering value of the centre data block group, and is stored to centre data block
In the filter value array of group.
3. TS-InSAR atmospheric phases filtering method as claimed in claim 2, which is characterized in that the sub-step D3 is specifically wrapped
It includes：The weighted value of the permanent scattering point of m-th of target isWherein, rm,nIt is permanently dissipated for m-th of target
The distance value of the filter center of exit point and n-th of adjacent region data block；
N-th of Neighborhood Filtering valueN ∈ [1,2 ..., N], N are the quantity of adjacent region data block；m∈
[1,2,…,Mn], MnQuantity for the corresponding permanent scattering point of target of n-th of adjacent region data block；Q (m) is permanent for m-th of target
The last phase place value of scattering point.
4. TS-InSAR atmospheric phases filtering method as claimed in claim 3, which is characterized in that the step F is specifically included：
The filter value of permanent scattering pointS0For the centre data block group centered on the data block where the permanent scattering point
Center filter value；Data chunk adjacent region data block sum, S centered on n ∈ [1,2 ..., N], NnFor this forever
N-th of Neighborhood Filtering value of the centre data block group centered on data block where long scattering point.
5. TS-InSAR atmospheric phases filtering method as claimed in claim 2, which is characterized in that the sub-step D1 is specifically wrapped
It includes：Data block orientation points are Ra, take and are not less thanSmallest positive integral A, data block distance for Rb, takes and is not less than to pointsSmallest positive integral B, filter window data block total number is (2 × A) × (2 × B), and adjacent region data block is removes in filter window
All data blocks of residue outside centre data block group.
6. TS-InSAR atmospheric phases filtering method as claimed in claim 2, which is characterized in that sub-step D1 is specifically included：
The permanent scattering point for meeting standard in the data block refers to be less than filtering half in the data block with the distance of filter center
The permanent scattering point of diameter R.
7. TS-InSAR atmospheric phases filtering method as described in claim 1, which is characterized in that the permanent scattering point information
Quantity including the permanent scattering point in data block, the last phase place value of each permanent scattering point and location information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610265353.3A CN105954748B (en) | 2016-04-26 | 2016-04-26 | TS-InSAR atmospheric phase filtering methods based on partition strategy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610265353.3A CN105954748B (en) | 2016-04-26 | 2016-04-26 | TS-InSAR atmospheric phase filtering methods based on partition strategy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105954748A CN105954748A (en) | 2016-09-21 |
| CN105954748B true CN105954748B (en) | 2018-07-10 |
Family
ID=56916021
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610265353.3A Active CN105954748B (en) | 2016-04-26 | 2016-04-26 | TS-InSAR atmospheric phase filtering methods based on partition strategy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105954748B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106950556A (en) * | 2017-05-03 | 2017-07-14 | 三亚中科遥感研究所 | Heritage area deformation monitoring method based on distributed diffusion body sequential interference SAR technology |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA1257370A (en) * | 1984-09-07 | 1989-07-11 | Akira Maeda | Method of reconstructing images from synthetic aperture radar's data |
| US7515098B1 (en) * | 2002-01-08 | 2009-04-07 | Science Applications International Corporation | Method for developing and using an image reconstruction algorithm for multipath scattering |
| CN104199033A (en) * | 2014-09-15 | 2014-12-10 | 西安电子科技大学 | Phase gradient autofocus motion compensation method based on SAR image search |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001083243A (en) * | 1999-09-13 | 2001-03-30 | Mitsubishi Electric Corp | Extraction apparatus for three-dimensional information on landform by interference-type synthetic aperture radar |
| US9329264B2 (en) * | 2013-02-15 | 2016-05-03 | Raytheon Company | SAR image formation |
-
2016
- 2016-04-26 CN CN201610265353.3A patent/CN105954748B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA1257370A (en) * | 1984-09-07 | 1989-07-11 | Akira Maeda | Method of reconstructing images from synthetic aperture radar's data |
| US7515098B1 (en) * | 2002-01-08 | 2009-04-07 | Science Applications International Corporation | Method for developing and using an image reconstruction algorithm for multipath scattering |
| CN104199033A (en) * | 2014-09-15 | 2014-12-10 | 西安电子科技大学 | Phase gradient autofocus motion compensation method based on SAR image search |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105954748A (en) | 2016-09-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3853807B1 (en) | Generation of synthetic high-elevation digital images from temporal sequences of high-elevation digital images | |
| DE102015122825B4 (en) | Techniques for grouping target elements for object fusion | |
| CA3104652C (en) | Detection and replacement of transient obstructions from high elevation digital images | |
| CN109374537A (en) | The smelly identifying water boy method and device of urban black | |
| Li et al. | Knowledge-based trajectory completion from sparse GPS samples | |
| Moraitis et al. | Magnetic helicity and eruptivity in active region 12673 | |
| CN103902802B (en) | A kind of vegetation index time series data method for reconstructing for taking spatial information into account | |
| CN105005789B (en) | A kind of remote sensing images terrain classification method of view-based access control model vocabulary | |
| CN105004337B (en) | Agricultural unmanned plane autonomous navigation method based on matching line segments | |
| CN104463164A (en) | Tree canopy structure information extraction method based on rib method and crown height ratio | |
| Deluca et al. | Scale invariant events and dry spells for medium-resolution local rain data | |
| CN105607062B (en) | A kind of weather radar figure analysis system | |
| CN103970932A (en) | High-resolution permanent scatterer modeling method for separation of building and background | |
| CN108549080A (en) | A kind of transmission tower position extracting method and system | |
| CN103809180B (en) | For InSAR topographic Pre-Filter processing method | |
| CN106529472B (en) | Object detection method and device based on large scale high-resolution high spectrum image | |
| CN108345897A (en) | A kind of evaluation method of Scenic Bridges index | |
| CN105954748B (en) | TS-InSAR atmospheric phase filtering methods based on partition strategy | |
| CN103823219A (en) | Self-adaption iteration non-local interferometric synthetic aperture radar interferometric phase filtering method | |
| CN110222041A (en) | A kind of traffic data cleaning method restored based on tensor | |
| Zhang et al. | A study on coastline extraction and its trend based on remote sensing image data mining | |
| CN116091930A (en) | Terrace drawing method, device, equipment and storage medium based on remote sensing | |
| Koshak et al. | Estimates of the lightning NOx profile in the vicinity of the North Alabama Lightning Mapping Array | |
| Tasca et al. | The zCOSMOS redshift survey: evolution of the light in bulges and discs since z~ 0.8 | |
| CN109116423A (en) | A kind of diffraction multiple wave drawing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |