CN105954748A - TS-InSAR atmospheric phase filtering method based on blocking strategy - Google Patents
TS-InSAR atmospheric phase filtering method based on blocking strategy Download PDFInfo
- Publication number
- CN105954748A CN105954748A CN201610265353.3A CN201610265353A CN105954748A CN 105954748 A CN105954748 A CN 105954748A CN 201610265353 A CN201610265353 A CN 201610265353A CN 105954748 A CN105954748 A CN 105954748A
- Authority
- CN
- China
- Prior art keywords
- data block
- scattering point
- value
- filter
- permanent
- 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.)
- Granted
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 invention provides a TS-InSAR atmospheric phase filtering method based on blocking strategy. The method comprises the following steps: dividing the original SAR image data into equal data blocks and gathering the information of permanent scattering points; with the data blocks as a center to select a central data block group and based on the established filtering parameters and the information of permanent scattering points, calculating the central filtering value of the central data block group and the filtering value surrounding the central data block group; and based on the central filtering value of the central data block group and the filtering value surrounding the central data block group, obtaining the filtering value of the permanent scattering points. The present invention eliminates the need to repeatedly calculate the filter value of each permanent scatter point, greatly improving the filtering efficiency. The combination of a precisely filtering method and approximately filtering method ensures high precision while increasing the speed considerably. The method can be adaptive to different filtering radiuses and different data types.
Description
Technical field
The present invention relates to interference synthetic aperture radar technical field, be a kind of TS-InSAR based on partition strategy
Atmospheric phase filtering method.
Background technology
Synthetic Aperture Radar Technique is applied to Ground Deformation monitoring, and the theory proposed the earliest is that heavy rail difference is done
Relating to technology, but be limited by time and space decoherence and the impact of atmospheric oscillation the most in the same time, heavy rail is poor
Divide interference technique to be very limited for Ground Deformation monitoring, make application traditional just because of these restrictions
The interference synthetic aperture radar (InSAR) of heavy rail differential technique is extremely difficult to preferable meter level digital elevation model
And grade deformation monitoring (DEM), in order to solve these problems, time series interference technique (TS-InSAR)
Arise at the historic moment, the stronger Coherent Targets of coherence of its application limited quantity, namely Permanent scatterers
(permanent scatterer, PS), these PS points form the corner reflector net of " natural ", permissible
Efficiently against time and space decorrelation and the impact of atmospheric phase, real obtain meter level digital elevation mould
Type (DEM) and grade Ground Deformation data.Simultaneously as PS point is not by time and space decorrelation
Impact so that SAR image data breaches the restriction of original time and Space Baseline, available SAR
Image quantity is greatly increased, and is greatly improved data user rate, is also the integrated of different SAR image data
Provide condition.And time series interference technique key issue extract from residual phase exactly due to
Phase place produced by atmospheric oscillation, thus in differential phase, remove this atmospheric phase, it is thus achieved that high-precision
Non-linear deformation phase place.
The general fashion obtaining atmospheric phase at present from remaining difference interferometric phase is phase filtering technology.Profit
Uncorrelated with the time domain height of atmospheric phase, i.e. high frequency characteristics, spatial domain height auto-correlation, i.e. low frequency characteristic.
In time domain high-pass filtering, spatial domain low-pass filtering, obtain atmospheric phase.And for traditional weighted mean
Airspace filter technology, the longest, computationally intensive, along with the lifting of required precision, time complexity will drastically
Increasing, and there is substantial amounts of redundant operation, picture size is the biggest, and operation efficiency is the lowest, and these can not
The significant drawbacks ignored seriously restricts its application.Such as having 56 width images for one, picture size is less,
(distance to) × 1500 that are 400 (orientation to), for having the time series of 14190 PS points, pass
The filtering time of not method of partition of uniting is about 20s.And image is in units of scape in reality, every scape figure
The orientation of picture can reach tens thousand of magnitude to distance to sampled point, and PS point number reaches hundreds of thousands, uses tradition
Method filtering time reaches hour magnitude the most several days, forecasts, far from for disaster alarm or weather conditions
Disaster alarm and the real-time of weather conditions forecast and accuracy requirement can be reached.
In a word, it is high that existing phase filtering technology there is also time complexity, and operand is big, and redundant operation is many
Etc. shortcoming, therefore it is badly in need of developing a kind of quick for high-volume large scene SAR image data, in high precision,
Atmospheric phase filtering technique.
Summary of the invention
(1) to solve the technical problem that
In order to solve prior art problem, the invention provides a kind of TS-InSAR based on partition strategy big
Gas phase filtering method.
(2) technical scheme
The invention provides a kind of TS-InSAR atmospheric phase filtering method based on partition strategy, including:
Step A: original SAR image data is divided into data block；Step B: forever dissipating in statistical data block
Exit point information；Step C: Selection Center data chunk centered by a data block, based on the filtering ginseng set
Number and permanent scattering point information, calculate the center filter value of described centre data block group；Step D: based on setting
Fixed filtering parameter and permanent scattering point information, calculating centre data block group centered by described data block
Neighborhood Filtering value；Step E: all data blocks in SAR image are performed step C, D, and center is filtered
Wave number and Neighborhood Filtering value store the filter value array of the centre data block group centered by each data block；
And step F: the filter value number of the centre data block group centered by data block based on permanent scattering point place
Group, by center filter value and the field filter value of this filter value array, obtains the filter value of permanent scattering point.
(3) beneficial effect
From technique scheme it can be seen that the TS-InSAR atmospheric phase based on partition strategy of the present invention
Filtering method has the advantages that
(1) speed is fast；
Relative to traditional filtering method, this method utilizes partition strategy, by center filter value and the neighbour of data block
Territory filter value calculates before filtering, have only to during filtering by the two add up, it is not necessary to each forever
Scattering point all double counting filter values, substantially increase filtration efficiency, and speed promotes nearly 10 times；
(2) precision is high；
The present invention is directed to the centre data block group in filter window, use accurate filtering method, in order to promote
Speed, uses approximation filtering method for the adjacent region data block that centre data block group is corresponding, due to window center
Weight is relatively big, and edge weights is less, therefore adjacent region data block is the least due to the produced error of approximation, permissible
Ignore, therefore the present invention can ensure that the highest precision while significant increase speed；
(3) robustness is good；
Can well adapt to for different filter radius, when filter radius (distance to) less than 20, this
Invention can reduce block size with self adaptation, it is ensured that filtering accuracy, for different data type (real number,
Plural number), the present invention still can adapt to very well.
Accompanying drawing explanation
Fig. 1 is the stream of the TS-InSAR atmospheric phase filtering method based on partition strategy of the embodiment of the present invention
Cheng Tu.
Detailed description of the invention
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment,
And referring to the drawings, the present invention is described in more detail.It should be noted that describe at accompanying drawing or description
In, similar or identical part all uses identical figure number.The implementation not illustrated in accompanying drawing or describe,
For form known to a person of ordinary skill in the art in art.Although it addition, can provide herein comprise specific
The demonstration of the parameter of value, it is to be understood that parameter is worth equal to corresponding without definite, but can be acceptable
It is similar in error margin or design constraint be worth accordingly.The direction term mentioned in embodiment, such as " on ",
D score, "front", "rear", "left", "right" etc., be only the direction with reference to accompanying drawing.Therefore, the side of use
It is used to illustrate not for limiting the scope of the invention to term.
Embodiments provide a kind of TS-InSAR atmospheric phase filtering method based on partition strategy,
It specifically includes:
Step A: original SAR image data is divided into data block.Utilize partition strategy, by data block
Center filter value and Neighborhood Filtering value are calculated before filtering, have only to add up the two during follow-up filtering
Can, it is not necessary to each permanent scattering point double counting filter value, it is possible to be greatly improved filtration efficiency, experiment
Show that speed can promote nearly 10 times.
Step A specifically includes: for each width image data in several original SAR image data, will
This width image data is divided into equivalently-sized data block.
To several original SAR image data, first according to the size of original SAR image, original by every
SAR image data is divided into some equivalently-sized data blocks, namely image carries out uniform grid division, often
The corresponding data block of one grid, and each data block is numbered, the order of numbering is by left-to-right, by upper
Arrive down.
Wherein, equivalently-sized data block refers to that distance is to counting with orientation to the most identical data block of counting.
In order to ensure enough filtering accuracies, the robustness of Enhancement Method, the size of data block and the follow-up filter chosen
Ripple radius is directly proportional；Preferably, the size of data block be distance to 7 × orientation to 35 or distance to 6 × side
Position is to 30.
Step B: the permanent scattering point information in statistical data block.
Step B specifically includes: travel through a data block, identifies permanent scattering point and adds up this data block
Permanent scattering point information, thus obtain the permanent scattering of all data blocks of all original SAR image data
Dot information.
Preferably, data block is traveled through the orientation referring to ergodic data block line by line to point；Or by column
The distance of ergodic data block to point.
The quantity of the permanent scattering point in preferably, the permanent scattering point information of this data block includes this data block,
The last phase place value of each permanent scattering point and positional information.
Preferably, by coherence factor threshold method, amplitude threshold method or phase place deviation threshold method identification data block
Permanent scattering point.
Step C: Selection Center data chunk centered by a data block, based on the filtering parameter and forever set
Scattering point information for a long time, calculates the center filter value of centre data block group.
Step C specifically includes:
Sub-step C1: (2X-1) centered by this data block × (2X-1) individual data block organization center data block
Group, described X >=2, preferably 2 or 3, wherein, this data block is as central block, centre data block group a good appetite suddenly appearing in a serious disease
Other data blocks beyond heart block are periphery block.
Sub-step C2: using the forever scattering point of in central block as the filter of the filter window that filter radius is R
Ripple center.
Wherein, this filter radius R more than data block distance to counting and at least one in counting of orientation,
Filter window is made to contain multiple data block.
Sub-step C3: calculate the weighted value of all permanent scattering point of central block and periphery block.
Wherein, the weighted value of permanent scattering point is relevant with its positional information, i.e. permanent scattering point and filter center
The weighted value of distance and permanent scattering point be inversely proportional to.In central block, the weighted value of the permanent scattering point of kth isWherein, rkCentered by the distance value of the permanent scattering point of kth and filter center in block；
In i-th periphery block, the weighted value of the permanent scattering point of jth isWherein, rI, jFor i-th
The permanent scattering point of jth and the distance value of filter center in periphery block；This as filter center forever scatters
The weighted value of point is 1.
Sub-step C4: based on central block and the weighted value of all permanent scattering point of periphery block and last phase place value,
Calculate the centre data block group filter value of this permanent scattering point.
Wherein, the central block group filter value of this permanent scattering point
Wherein, the quantity of the permanent scattering point of block centered by K；Q (k) andK ∈ [1,2 ..., K], respectively center
The last phase place value of the permanent scattering point of block and weighted value；I is the quantity of periphery block；JiFor i-th periphery block
The quantity of permanent scattering point；Q (i, j) andI ∈ [1,2 ..., I], j ∈ [1,2 ..., Ji], respectively periphery block
The last phase place value of permanent scattering point and weighted value.
Sub-step C5: each permanent scattering point in central block is performed sub-step C2, C3 and C4,
The centre data block group filter value of all permanent scattering point in this central block, as centre data block group
Center filter value, and store to the filter value array of centre data block group.
Wherein, the filter value array of this centre data block group be F (l, k), l ∈ [1,2 ..., L], wherein centered by l
The numbering of data chunk, this numbering takes the numbering of the central block of this centre data block group, L every scape SAR image institute
The data block total number comprised；K ∈ [1,2 ..., K], centered by K, the quantity of the permanent scattering point of block, works as central block
After determining, this filter value array comprises K filter value.
Aforesaid way utilizes the centre data block group including that the centre data block group of periphery block calculates permanent scattering point
Filter value, filtering accuracy is high.
In other embodiments, described X takes 1, and described centre data block group only includes this data block, does not wraps
Containing periphery block, only calculate the weighted value of all permanent scattering points of central block, and all permanent scattered based on central block
The weighted value of exit point and last phase place value, calculate the centre data block group filter value of permanent scattering point, and will forever
The centre data block group filter value of scattering point stores to the filter value array of centre data block group for a long time, so exists
While ensureing certain filtering accuracy, reduce algorithm complex, improve arithmetic speed.
Specific as follows:
Sub-step C2: using the forever scattering point of in central block as the filter of the filter window that filter radius is R
Ripple center.
Wherein, this filter radius R more than data block distance to counting and at least one in counting of orientation,
Filter window is made to contain multiple data block.
Sub-step C3: calculate the weighted value of all permanent scattering points of central block.
Wherein, the weighted value of permanent scattering point is relevant with its positional information, i.e. permanent scattering point and filter center
The weighted value of distance and permanent scattering point be inversely proportional to, in central block, the weighted value of the permanent scattering point of kth isWherein, rkCentered by the distance value of the permanent scattering point of kth and filter center in block,
Weighted value as this permanent scattering point of filter center is 1.
Sub-step C4: the weighted value of all permanent scattering points based on central block and last phase place value, calculating should
The centre data block group filter value of permanent scattering point.
Wherein, the central block filter value of this permanent scattering pointWherein, block centered by K
The quantity of permanent scattering point；Q (k) andK ∈ [1,2 ..., K], the respectively permanent scattering point of central block
Last phase place value and weighted value.
Sub-step C5: each permanent scattering point in central block is performed sub-step C2, C3 and C4,
The centre data block group filter value of all permanent scattering point in this central block, as centre data block group
Center filter value, and store to the filter value array of centre data block group.
Wherein, the filter value array of this centre data block group be F (l, k), l ∈ [1,2 ..., L], wherein centered by l
The numbering of block, the data block total number that L every scape SAR image is comprised；K ∈ [1,2 ..., K], block centered by K
The quantity of permanent scattering point, after central block determines, this filter value array comprises K filter value.
Step D: based on the filtering parameter set and permanent scattering point information, calculates centered by this data block
The Neighborhood Filtering value of centre data block group.
Sub-step D1: using permanent in this data block scattering point as the filter window that filter radius is R
Filter center, chooses the adjacent region data block of this centre data block group.
Preferably, the filter radius of filter window is R, data block orientation to count as Ra, take and be not less thanSmallest positive integral A, data block distance to count as Rb, take and be not less thanSmallest positive integral B, filter
Ripple window data block sum is (2 × A) × (2 × B), and adjacent region data block is removing centre data in filter window
The all data blocks of residue outside block group.
Sub-step D2: again using the permanent scattering point in center of the n-th adjacent region data block as filter center, choose
The permanent scattering point of standard is met as the permanent scattering point of target in this data block.
Preferably, the described permanent scattering point meeting standard refers in this data block that the distance with filter center is little
Permanent scattering point in filter radius R.
From sub-step D1 and D2, for being in the data block on filter window border, i.e. part is permanent dissipates
The data block that exit point is positioned at filter window, the permanent scattering point of part is positioned at outside filter window, the present invention is permissible
Permanent scattering point within only utilization is in filter radius is filtered processing, i.e. with filter center as reference point,
Based on filter radius criterion, the useful permanent scattering point being accurately determined in data boundary block, get rid of useless forever
Scattering point, further increases filtering degree of accuracy for a long time.
Sub-step D3: calculate the weighted value of the permanent scattering point of target, weighted value based on the permanent scattering point of target
With the n-th Neighborhood Filtering value that residual phase is worth to this data block.
Wherein, the weighted value of the permanent scattering point of target is relevant with its positional information, i.e. the permanent scattering point of target and
The weighted value of the distance of filter center scattering point permanent with target is inversely proportional to.The permanent scattering point of m-th target
Weighted value isWherein, rM, nFor the permanent scattering point of m-th target and the n-th neighborhood
The distance value of the filter center of data block.
Wherein, the n-th Neighborhood Filtering valueN is adjacent region data
The quantity of block；M ∈ [1,2 ..., Mn], MnIt it is the number of the permanent scattering point of target that the n-th adjacent region data block is corresponding
Amount；Q (m) is the last phase place value of the permanent scattering point of m-th target.
Sub-step D4: all adjacent region data blocks of this centre data block group are performed sub-step D2 and D3,
Obtain the filter value of this centre data block group each adjacent region data block, as the Neighborhood Filtering of centre data block group
Value, and it is deposited in step C the filter value array of this centre data block group.
Wherein, this filter value array be F (l, K+n), l ∈ [1,2 ..., L], n ∈ [1,2 ..., N], L is every scape SAR
The data block total number that image is comprised；N is the sum of adjacent region data block.
Step E: all data blocks in SAR image are performed step C, D, and by center filter value and neighbour
Territory filter value stores the filter value array of the centre data block group centered by each data block.
Wherein, the filter value array of the centre data block group centered by each data block as F (l, k+n), l ∈
[1,2 ..., L], k ∈ [1,2 ..., K], n ∈ [1,2 ..., N], the data block total number that wherein L is comprised by every scape SAR；
K is the sum of the permanent scattering point of each data block, and N is the sum of this data block adjacent region data block；Each
K and N of data block there may be difference.
In step C and D, the present invention is directed to the centre data block group in filter window, use accurate filtering
Method, in order to promote speed, uses approximation filtering method for the adjacent region data block that centre data block group is corresponding,
Owing to window center weight is relatively big, edge weights is less, therefore adjacent region data block is due to the produced error of approximation
The least, can ignore, therefore the present invention can ensure that the highest precision while significant increase speed.
Step F: the filter value number of the centre data block group centered by data block based on permanent scattering point place
Group, by center filter value and the field filter value of this filter value array, obtains the filter value of this permanent scattering point.
Wherein, the filter value of permanent scattering pointThe filter value of the most permanent scattering point is that this forever dissipates
The center filter value of the centre data block group centered by the data block at exit point place and Neighborhood Filtering value sum, its
In,Data chunk adjacent region data block sum centered by N, wherein, SnFor
N-th Neighborhood Filtering value of the centre data block group centered by the data block at this permanent scattering point place.Meanwhile,
SnAlso it is filter value array F (L, K '+N ') of centre data block group centered by the n-th adjacent region data block
(l ', K '+n ') individual data value, and l ' ∈ [1,2 ..., L] it is the numbering of the n-th adjacent region data block；N ' is for being somebody's turn to do
The central block filter value storage numbering that adjacent region data block is corresponding, now central block regards the neighbour of this adjacent region data block as
Numeric field data block；K ' is the sum of scattering point permanent in this adjacent region data block；N ' is corresponding for this FIELD Data block
FIELD Data block sum.
In order to illustrate further advantages of the present invention, with filter radius R=25, (distance is to sampled point below
Number) as a example by illustrate that the inventive method has used compared to traditional filtering method advantage in speed, image
The 56 width satellite-borne SAR time series interference images registrated, imaging time is 1992 to 2002,
The size of every width picture (distance to) × 1500 that are 400 (orientation to), block size be 6 (distance to) ×
30 (orientation to), have a clear superiority in from the relatively traditional method of the inventive method seen from following table.
Table 1 the inventive method and traditional method time comparing result (filter radius 20-50)
As it can be seen from table 1 the filtering method that the present invention provides can well be fitted for different filter radius
Should, when filter radius (distance to) less than 20, the present invention can reduce block size with self adaptation, protect
Card filtering accuracy.It addition, for different data types (real number, plural number), the present invention still can be fine
Adapt to.
So far, already in connection with accompanying drawing, the present embodiment has been described in detail.According to above description, this area
The TS-InSAR atmospheric phase filtering method based on partition strategy of the present invention should have been had clearly by technical staff
The understanding of Chu.
It should be noted that in accompanying drawing or description text, the implementation not illustrating or describing, it is
In art, form known to a person of ordinary skill in the art, is not described in detail.Additionally, it is above-mentioned right
The definition of each element is not limited in the various modes mentioned in embodiment, and those of ordinary skill in the art can be right
It is changed simply or replaces, such as:
(1) the direction term mentioned in embodiment, such as " on ", D score, "front", "rear", " left ",
" right " etc., are only the directions with reference to accompanying drawing, are not used for limiting the scope of the invention；
(2) above-described embodiment can based on design and the consideration of reliability, being mixed with each other collocation use or and other
Embodiment mix and match uses, and the technical characteristic in i.e. different embodiments can freely form more reality
Execute example.
Particular embodiments described above, is entered the purpose of the present invention, technical scheme and beneficial effect
One step describes in detail, be it should be understood that the specific embodiment that the foregoing is only the present invention, not
For limiting the present invention, all within the spirit and principles in the present invention, any amendment of being made, equivalent,
Improve, should be included within the scope of the present invention.
Claims (10)
1. a TS-InSAR atmospheric phase filtering method based on partition strategy, it is characterised in that
Including:
Step A: original SAR image data is divided into data block；
Step B: the permanent scattering point information in statistical data block；
Step C: Selection Center data chunk centered by a data block, based on the filtering parameter set
With permanent scattering point information, calculate the center filter value of described centre data block group；
Step D: based on the filtering parameter set and permanent scattering point information, calculates with described data block
Centered by the Neighborhood Filtering value of centre data block group；
Step E: all data blocks in SAR image are performed step C, D, and by center filter value
With the filter value array that Neighborhood Filtering value stores centre data block group centered by each data block；
And
Step F: the filtering of the centre data block group centered by data block based on permanent scattering point place
Value array, by center filter value and the field filter value of this filter value array, obtains permanent scattering point
Filter value.
2. TS-InSAR atmospheric phase filtering method as claimed in claim 1, it is characterised in that
Described step C specifically includes:
Sub-step C1: (2X-1) centered by this data block × (2X-1) individual data block organization center number
According to block group, this data block is as central block, centre data block group other data blocks in addition to central block
For periphery block, described X is natural number；
Sub-step C2: be the filter window of R as filter radius using permanent in central block scattering point
Filter center；
Sub-step C3: calculate the weighted value of all permanent scattering point of central block and periphery block；
Sub-step C4: based on central block and the weighted value of all permanent scattering point of periphery block and remnants
Phase value, calculates the centre data block group filter value of this permanent scattering point；And
Sub-step C5: each permanent scattering point in central block is performed sub-step C2, C3 and C4,
Obtain the centre data block group filter value of all permanent scattering point in this central block, as calculation in this
According to the center filter value of block group, and store to the filter value array of centre data block group.
3. TS-InSAR atmospheric phase filtering method as claimed in claim 2, it is characterised in that
Described sub-step C3 specifically includes: in central block, the weighted value of the permanent scattering point of kth isWherein, rkCentered by the distance of the permanent scattering point of kth and filter center in block
Value；In i-th periphery block, the weighted value of the permanent scattering point of jth isWherein,
rI, jDistance value for the permanent scattering point of jth in i-th periphery block Yu filter center.
4. TS-InSAR atmospheric phase filtering method as claimed in claim 3, it is characterised in that
Described sub-step C4 specifically includes: the central block group filter value of this permanent scattering pointWherein, the permanent scattering point quantity of block centered by K；
Q (k) andK ∈ [1,2 ..., K], the respectively last phase place value of the permanent scattering point of central block and weight
Value；I is the quantity of periphery block；JiPermanent scattering point quantity for i-th periphery block；
Q (i, j) andI ∈ [1,2 ..., I], j ∈ [1,2 ..., Ji], the respectively remnants of the permanent scattering point of periphery block
Phase value and weighted value.
5. TS-InSAR atmospheric phase filtering method as claimed in claim 4, it is characterised in that
Described step D specifically includes:
Sub-step D1: be the spectral window of R as filter radius using permanent in this data block scattering point
The filter center of mouth, chooses the adjacent region data block of this centre data block group；
Sub-step D2: again using the permanent scattering point in center of the n-th adjacent region data block as filter center,
The permanent scattering point of standard is met as the permanent scattering point of target in choosing this data block；
Sub-step D3: calculate the weighted value of the permanent scattering point of target, based on the permanent scattering point of target
Weighted value and residual phase are worth to the n-th Neighborhood Filtering value of this data block；And
Sub-step D4: all adjacent region data blocks of this centre data block group are performed sub-step D2 and
D3, obtains the filter value of this centre data block group each adjacent region data block, as this centre data block group
Neighborhood Filtering value, and store to the filter value array of centre data block group.
6. TS-InSAR atmospheric phase filtering method as claimed in claim 5, it is characterised in that
Described sub-step D3 specifically includes: the weighted value of the permanent scattering point of m-th target isWherein, rM, nFor the permanent scattering point of m-th target and the n-th adjacent region data
The distance value of the filter center of block；
N-th Neighborhood Filtering valueN ∈ [1,2 ..., N], N is adjacent region data block
Quantity；M ∈ [1,2 ..., Mn], MnIt it is the permanent scattering point of target that the n-th adjacent region data block is corresponding
Quantity；Q (m) is the last phase place value of the permanent scattering point of m-th target.
7. TS-InSAR atmospheric phase filtering method as claimed in claim 6, it is characterised in that
Described step F specifically includes: the filter value of permanent scattering pointS0Forever scatter for this
The center filter value of the centre data block group centered by the data block at some place；
N ∈ [1,2 ..., N], data chunk adjacent region data block sum, S centered by NnFor this permanent scattering point place
Data block centered by the n-th Neighborhood Filtering value of centre data block group.
8. TS-InSAR atmospheric phase filtering method as claimed in claim 5, it is characterised in that
Described sub-step D1 specifically includes: data block orientation, to counting as Ra, takes and is not less thanMinimum
Integer A, data block distance, to counting as Rb, takes and is not less thanSmallest positive integral B, filter window number
Being (2 × A) × (2 × B) according to block sum, adjacent region data block is removing centre data block in filter window
The all data blocks of residue outside group.
9. TS-InSAR atmospheric phase filtering method as claimed in claim 5, it is characterised in that
Sub-step D1 specifically includes:
Meet in described data block the permanent scattering point of standard refer in this data block with filter center
Distance is less than the permanent scattering point of filter radius R.
10. TS-InSAR atmospheric phase filtering method as claimed in claim 1, it is characterised in that
Described permanent scattering point information includes the quantity of the permanent scattering point in data block, each permanent scattering point
Last phase place value and positional 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 true CN105954748A (en) | 2016-09-21 |
| CN105954748B 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) |
Cited By (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 (5)
| 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 |
| 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 |
| US7515098B1 (en) * | 2002-01-08 | 2009-04-07 | Science Applications International Corporation | Method for developing and using an image reconstruction algorithm for multipath scattering |
| WO2014126638A1 (en) * | 2013-02-15 | 2014-08-21 | Raytheon Company | Sar image formation |
| 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 |
-
2016
- 2016-04-26 CN CN201610265353.3A patent/CN105954748B/en active Active
Patent Citations (5)
| 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 |
| 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 |
| US7515098B1 (en) * | 2002-01-08 | 2009-04-07 | Science Applications International Corporation | Method for developing and using an image reconstruction algorithm for multipath scattering |
| WO2014126638A1 (en) * | 2013-02-15 | 2014-08-21 | Raytheon Company | Sar image formation |
| 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 |
Cited By (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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105954748B (en) | 2018-07-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Keshtkar et al. | Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery | |
| CN108846352B (en) | Vegetation classification and identification method | |
| Zhang et al. | Boundary-constrained multi-scale segmentation method for remote sensing images | |
| Häring et al. | Spatial disaggregation of complex soil map units: a decision-tree based approach in Bavarian forest soils | |
| CN109344812B (en) | Improved cluster-based single photon point cloud data denoising method | |
| CN108629287A (en) | A kind of remote sensing image terrain classification method | |
| DE112018004214T5 (en) | Scalable merging of space-time density data | |
| CN106780737A (en) | A kind of method that utilization digital elevation model calculates Geomorphologic Instantaneous Unit Hydrograph probability | |
| CN108664838A (en) | Based on the monitoring scene pedestrian detection method end to end for improving RPN depth networks | |
| CN110598513A (en) | Urban development boundary prediction method based on SLUTH model | |
| CN104463164A (en) | Tree canopy structure information extraction method based on rib method and crown height ratio | |
| CN103871102A (en) | Road three-dimensional fine modeling method based on elevation points and road outline face | |
| Tarantino et al. | Extracting buildings from true color stereo aerial images using a decision making strategy | |
| CN103927737A (en) | SAR image change detecting method based on non-local mean | |
| Pickl et al. | Dialectometric concepts of space: Towards a variant-based dialectometry | |
| CN109363697A (en) | A kind of method and device of breast image lesion identification | |
| CN109784602A (en) | A kind of disaster-ridden kind of coupling physical vulnerability assessment method based on PTVA model | |
| Dai et al. | Attention based simplified deep residual network for citywide crowd flows prediction | |
| Zhang et al. | A study on coastline extraction and its trend based on remote sensing image data mining | |
| Militino et al. | Filling missing data and smoothing altered data in satellite imagery with a spatial functional procedure | |
| CN104794528A (en) | Rocky desertification governance model case library management system and rocky desertification governance model case library management method | |
| Ren et al. | Improved unet combining dropout and acnet for remote sensing image change detection | |
| Qian et al. | Spatial contextual noise removal for post classification smoothing of remotely sensed images | |
| CN103377477B (en) | A kind of high-resolution remote sensing image multilayer division method | |
| CN105954748A (en) | TS-InSAR atmospheric phase filtering method based on blocking strategy |
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 |