CN109903352A - A kind of seamless orthography production method in the big region of satellite remote-sensing image - Google Patents
A kind of seamless orthography production method in the big region of satellite remote-sensing image Download PDFInfo
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Abstract
The present invention relates to a kind of seamless orthography production methods in the big region of satellite remote-sensing image, including the following contents: 1) constructing remote sensing image imaging geometry model；2) remote sensing image and dem data to be matched are read, DEM and remote sensing image data subject to registration are matched；3) remote sensing image ortho-rectification obtains corresponding DOM product；4) according to the positional relationship obtained in step 3) between orthography, determine the overlapping region between image, it matches to obtain same place in overlapping region, the error equation based on TPS model is established to every group of same place, the reduction of geometrical deviation between image is obtained by iterative solution；5) even light is carried out to orthography after all corrections and inlayed, obtain the orthophotomap product in big region.The present invention introduces thin plate spline model during orthography adjustment, and the smaller caused conventional geometric correction model accuracy of overlapping range is not high between solving the problems, such as image.
Description
Technical field
The present invention relates to a kind of seamless orthography production methods in the big region of satellite remote-sensing image, are related in photogrammetric
Remote sensing image technical field.
Background technique
Digital orthophoto map (Digital Orthophoto Map, DOM) is the important composition of tradition mapping 4D product,
It is to reflect that the important carrier and remotely-sensed data of topography and geomorphology and characters of ground object are served a kind of bases of all trades and professions and produced
Product.The process for generating digital orthoimage is usually to utilize digital elevation model (Digital Elevation Model, DEM)
Remote sensing image correct by the height displacement of pixel, various distortion and displacement error is eliminated, finally obtains comprising geography information
With the satellite remote-sensing image product of various special topics.By sensor internal optical system when the remote sensing image of original acquisition is due to imaging
Distortion, scanning system is non-linear, external attitude variation, the influence of earth curvature and hypsography etc., has different degrees of abnormal
Become and be distorted, so as to cause projection image's product that the image direct splicing with certain degree of overlapping generates, at edge fit position
There is the inconsistent situation in geometric position.Further, since the various errors introduced in imaging process, can also cause image and have
Position deviation between altitude data, so that the precision constituted to height displacement and landform impacts.In the moon and Mars track
During device is photogrammetric, high-precision benchmark and ground control shortage are caused between image between image and altitude data not
Consistency problem is more prominent, the final engineering and research application research for influencing map products.
Existing method obtains control point using existing DOM product as medium between image and DEM, by by control point
Tie point between all images is included in block adjustment simultaneously, is resolved using adjustment and is carried out to the imaging geometry model of image
It refines, the final Geometrical consistency improved between image between image and DEM；Then using after refining geometrical model and DEM pairs
Image carries out ortho-rectification.But when handling big area image data, since the drawing range being related to is wider, includes image number
Amount is more, control data precision is unsatisfactory for requiring etc., will lead to that block adjustment calculation amount is excessive, resolving is unstable, iteration is difficult
The problems such as requiring is unsatisfactory for convergence, adjustment precision.In addition, if entire area of the overlapping range relative to image between image
For it is too small if, traditional block adjustment method based on multinomial model is difficult to the adjustment precision met the requirements.
Summary of the invention
It is put down in view of the above-mentioned problems, making adjustment iteration be easy to restrain and be conducive to raising the object of the present invention is to provide one kind
Orthography production method that the big region of the satellite remote-sensing image of poor precision is seamless.
To achieve the above object, the present invention takes following technical scheme: a kind of big region of satellite remote-sensing image is seamless just to be penetrated
Making video method, including the following contents:
1) remote sensing image imaging geometry model is constructed；
2) remote sensing image and dem data to be matched are read, DEM and remote sensing image data subject to registration are matched；
3) remote sensing image ortho-rectification obtains corresponding DOM product；
4) according to the positional relationship obtained between orthography in step 3), the overlapping region between image is determined, in overlay region
Domain matches to obtain same place, establishes the error equation based on TPS model to every group of same place, is obtained between image by iterative solution
The reduction of geometrical deviation；
5) even light is carried out to orthography after all corrections and inlayed, obtain the orthophotomap product in big region.
Further, matching is carried out to DEM and remote sensing image data subject to registration to constrain jointly using geometry with radiation information
DEM matched with image, detailed process are as follows:
2.1) DEM is generated into analog image, as reference images；
2.2) affine-Scale invariant features transform algorithm is utilized, reference images and remote sensing image initial matching point are obtained；
2.3) initial matching point is utilized, rational function model is calculated and initially refines parameter；
2.4) the matched initial value of least square essence is obtained；
2.5) based on the Least squares matching of global restriction, export remote sensing image to be matched radiation deformation parameter and
Geometry deformation parameter.
Further, above-mentioned steps 2.1) DEM generated into analog image, as reference images, detailed process are as follows:
2.1.1) on DEM, each pixel is calculated pixel-by-pixel in the gradient p and q in x Yu the direction y；
2.1.2) according to gradient value, the gradient, slope aspect value are calculated pixel-by-pixel:
A=p/q
In formula, S is value of slope, and A is slope aspect value；
2.1.3) according to the header file information of remote sensing image data, obtain solar azimuth when remote sensing image obtains with too
Positive elevation angle, the empty image of creation and DEM same size calculate its corresponding simulation radiation value information pixel-by-pixel, and are stored in sky
Image, building obtain reference images.
Further, above-mentioned steps 2.2) affine-Scale invariant features transform algorithm is utilized, obtain reference images and remote sensing
Image initial matching point, detailed process are as follows:
The sample sequence for obtaining video camera longitude angle and angle of latitude first, all may be made for simulating by camera optical axis
At affine deformation come realize image convert；
Then remote sensing image to be matched is subjected to Sloped rotating transformation and generates analog image；
The analog image of generation is finally subjected to characteristic point detection and reference images according to Scale invariant features transform algorithm
Matching obtains the initial matching point between remote sensing image and reference images.
Further, above-mentioned steps 2.4) obtain the detailed process of the matched initial value of least square essence are as follows:
2.4.1 characteristic point) is extracted in reference images using feature point extraction operator；
2.4.2) using the rational function model refined, the characteristic point in reference images is calculated onto remote sensing image, is made
For Least squares matching initial value.
Further, above-mentioned steps 2.5) Least squares matching based on global restriction, export remote sensing image to be matched
Radiation deformation parameter and geometry deformation parameter, detailed process are as follows:
2.5.1 it) is solving remote sensing image and while reference images registration parameter, is resolving rational function model and refine ginseng
It is as follows to introduce global restriction Least squares matching model for number:
g1(x,y)+n1(x, y)=h0+h1g2(r,c)+n2(x,y)
R=ar 0+ar 1r′+ar 2c′+δr
C=bc 0+bc 1r′+bc 2c′+δc
In formula, g1With g2For the radiation value function of benchmark image and remote sensing image tie point respective coordinates position subject to registration, n1
With n2For the function of benchmark image and remote sensing image noise subject to registration, (x, y) is coordinate of the tie point in reference images, h0, h1
Radiation deformation parameter between benchmark image and remote sensing image, ar 0……ar 2,bc 0……bc 2For geometry deformation parameter (ginseng of refining
Number), (r ', c ') is projection coordinate of the ground point using RFM back projection to image space, and (r, c) is that the projection of back projection to image space is sat
The picpointed coordinate that mark obtains after refining, δr,δcFor model error；
2.5.2) according to global restriction Least squares matching model foundation error equation:
vg=h0+h1g2(r,c)-g1(x,y)
vr=ar 0+ar 1r′+ar 2c′+δr-r
vc=bc 0+bc 1r′+bc 2c′+δc-c
In formula, vgFor the error of equation radiation value between being registrated image, vrError for equation in column direction, vcFor equation
In the error of line direction；
First order Taylor expansion is carried out to error equation, the error equation linearized:
In formula,For 0 item of Taylor series,Respectively indicate the one of column direction and line direction coordinate
Rank partial derivative；
2.5.3) to the matching initial value point of each input according to error equation, node-by-node algorithm normal equation；
2.5.4 normal equation) is solved, unknown number is corrected, and judge the related coefficient being calculated after adjustment, if
The sum of all Point correlation coefficients is met the requirements, then exports adjustment result, otherwise enters step 2.5.2 iterative solution；
2.5.5 the radiation deformation parameter and geometry deformation parameter for) exporting remote sensing image, complete remote sensing image and benchmark shadow
Total least squares matching as between.
Further, above-mentioned steps 3) remote sensing image ortho-rectification use positive solution and anti-solution.
Further, using anti-solution to the detailed process of remote sensing image ortho-rectification are as follows:
3.1) setting will generate the X-direction and Y-direction resolution ratio (R of orthographyxdom, Rydom), utilize raw video size
And the geometrical model of image, the coverage area of image is calculated, the upper left angular coordinate (X of image is set according to the range of image0,
Y0), if the picpointed coordinate of any point pixel center P is (x on pre-generatmg DOMdom,ydom), calculate the corresponding ground coordinate of P point
(X,Y)；
X=X0+Rxdom*xdom
Y=Y0+Rydom*ydom
3.2) it utilizes (X, Y) and DEM, interpolation to go out the elevation Z of the point, utilizes the video imaging geometry mould after establishing and refining
Type calculates corresponding picpointed coordinate p (x, y) on original image；
X=f1 (X, Y, Z)
Y=f2 (X, Y, Z)
In formula, f1 and f2 represent the video imaging geometrical model after refining, and Z indicates the elevation of P point；
3.3) gray scale interpolation acquires the gray value g (x, y) of picture point p；
3.4) the P point being assigned to the gray value of picture point p on the pixel namely DOM after correction；
3.5) above-mentioned operation successively is completed to pixel each on DOM, the DOM product by topographical correction can be obtained.
Further, above-mentioned steps 4) according to the positional relationship obtained in step 3) between orthography, it determines between image
Overlapping region matches to obtain same place in overlapping region, then establishes the error approach based on TPS model to every group of same place,
The reduction of geometrical deviation between image, detailed process are obtained by iterative solution are as follows:
4.1) according to the range information for obtaining orthography in step 3), the overlapping region between image is determined, wherein image
Overlay region be geographical coordinate rectangular extent intersection, obtain same place in overlapping region；
4.2) its upper left angular coordinate and resolution ratio are read from DOM header file, the identical point coordinates solution that matching is obtained
It asks to obtain ground coordinate:
4.3) it establishes and adjustment error equation is just penetrated based on TPS model, and solved using the principle of least square, obtained
Geometry between orthography is refined parameter.
Further, above-mentioned steps 4.3) it establishes and adjustment error equation is just penetrated based on TPS model, and use least square
Principle is solved, and is obtained the geometry between orthography and is refined parameter, detailed process are as follows:
4.3.1 the model of TPS) is established:
In formula, ax 0,ax 1,ax 2,by 0,by 1,by 2,δi(i=1,2 ... n) be TPS model parameter, x, y are original coordinates,
X', y' are the coordinate after correction, and n is the quantity of same place, ψ (ri) it is radial basis function:
In formula, r is any point in image to the Euclidean distance of matching same place；
4.3.2 the same place of error equation) based on TPS model for each Xie Qiuzhi object space, wherein be based on TPS mould
The error equation of type:
In formula,The respectively coordinate residual error in ranks direction,For original object coordinates,
For the object coordinates after refining, i indicates that piont mark, j indicate video number, and n indicates the sum of the same place on the image；
It is expressed as follows with matrix form:
V=Ax-L
L=[Dx1 Dy1]T
4.3.3) for point range Liru of the same name each of on every image shown on error equation, according to least square original
Reason solves and obtains the coefficient of TPS model；
4.4) obtained TPS model is solved according to 4.3) middle, correction and resampling is carried out to every image, after obtaining correction
Orthography.
Further, above-mentioned steps 4.4) every image is corrected and resampling is using positive solution or anti-solution；Its
In, correction is carried out to every image and resampling uses the detailed process of positive solution are as follows:
4.4.1 the upper left angle point ground coordinate and resolution ratio of orthography after correcting) are determined:
In formula, (X0,Y0) it is original orthography upper left angle point ground coordinate, (X0',Y0') it is orthography after correcting
Upper left angle point ground coordinate；
4.4.2) for every bit (x on original orthographydom,ydom), convert thereof into ground coordinate (X, Y).
4.4.3 coordinates correction) is carried out to X, Y, the coordinate X', Y' after obtaining its correction；
4.4.4 image coordinate (the x of DOM after) converting the ground coordinate after correction to correctiondom',ydom'):
xdom'=(X'-X0')/mx'
ydom'=(Y'-Y0')/my'
In formula, (mx',my') be correction after DOM resolution ratio, (X0’,Y0') sat for the upper left angle point ground of DOM after correction
Mark；
4.4.5) by (xdom,ydom) gray value be assigned to (xdom',ydom'), above-mentioned correction course is completed pixel-by-pixel, finally
Orthography after gray scale resampling is corrected is carried out to DOM.
The invention adopts the above technical scheme, which has the following advantages:
1, original remote sensing image is registrated by the seamless orthography system in big region of the invention with DEM first, then
It solves the problems, such as since geometric accuracy is unsatisfactory for requiring between control data precision deficiency bring orthography, by unknown number
Resolving is decomposed, and is reduced the unknown number quantity of single solution, so that adjustment iteration is easy to restrain, and is conducive to mention
High adjustment precision；
2, the invention proposes the error compensation method based on orthography, raw video is projected using imaging geometry model
Achieve the effect that dimension-reduction treatment, solve because intersection angle is small in traditional error compensation method, adjustment caused by geometry intensity is insufficient is unstable
Problem that is fixed, solving morbid state.
3, the present invention introduces thin plate spline model during orthography adjustment, solve between image overlapping range compared with
Conventional geometric caused by small corrects the not high problem of model accuracy；
4, the present invention proposes DEM and the directly matched method of image, solves traditional DEM and Image Matching needs in the process
The problem of known DOM is assisted, and in DEM and the ginseng of refining for obtaining video imaging geometrical model during Image Matching simultaneously
Number, so that the precision and quality that image is just penetrating correction gets a promotion.
Detailed description of the invention
Fig. 1 is the flow diagram of the seamless orthography production method in the big region of satellite remote-sensing image of the present invention；
Fig. 2 is the window schematic diagram of the embodiment of the present invention 3 × 3.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiments of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
Member's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As shown in Figure 1, the seamless orthography production method in the big region of satellite remote-sensing image proposed by the present invention, detailed process
Are as follows:
1, remote sensing image imaging geometry model is constructed
Currently used remote sensing image imaging geometry model is broadly divided into stringent imaging geometry model and universal imaging geometry
Model.Stringent imaging geometry model is the mathematical model with tight theoretical basis, it, can be with mainly based on collinearity equation
Accurately express the stringent geometrical relationship between image coordinate and ground point space coordinate.Universal imaging geometrical model is then avoided
The complex relationship of imaging process, the correlation being fitted using mathematical model between picpointed coordinate and object space point three-dimensional coordinate,
Common model of fit has average polynomial, direct linear transformation's model and rational function model (Rational
Function Model, RFM) etc., wherein rational function model is since its fitting precision is high, versatility is good, excellent using facilitating etc.
Point becomes most widely used a kind of mathematical model in the general geometrical model of remote sensing image.The present invention can be using any tight
Lattice imaging geometry model and universal imaging geometrical model.
The present invention is with the satellite image of the current highest resolution of the moon --- the imaging geometry model construction of LRO NAC image
For, the building process of stringent the imaging geometry model and general geometrical model that the present invention will be described in detail.Stringent imaging geometry mould
The building of type generally comprises two processes of interior orientation and outer orientation, and the building of general geometrical model is then needed with the stringent of building
Based on imaging geometry model, detailed process are as follows:
1.1) building of the stringent imaging geometry model of LRO NAC
1.1.1) LRO NAC interior orientation
From the IK secondary file of LRO obtain NAC camera interior orientation parameter for example: focal length, ranks direction centre coordinate,
Pixel dimension and distortion parameter etc. carry out interior orientation to NAC camera, wherein LRO then according to the distortion model of LRO NAC
The distortion model of NAC are as follows:
Xd=(sample-BORESIGHT_SAMPLE) * PIXEL_PITCH
R=xd
Xc=xd/ (1+k1*r2) (1)
In formula, sample is column coordinate of the picture point in NAC EDR initial data, and BORESIGHT_SAMPLE is column direction
Centre coordinate, PIXEL_PITCH is the pixel dimension of column direction, and xd is the coordinate (measure coordinate) comprising photogrammetric distortion, and k1 is
Radial distortion parameter, r are distance of the picture point to principal point, and xc is the coordinate for correcting rear image point in focal plane, unit mm.Due to
NAC is CCD linear array scanning camera, therefore similar the parameter yd=0, yc=0 of line direction.
1.1.2 it) is oriented outside LRO NAC
(a) collinearity equation is established
After the completion of interior orientation, coordinate of the available each pixel on focal plane after distortion correction, outer orientation is
It establishes focal plane coordinate system and star consolidates the relationship of coordinate system, stringent imaging geometry model can be expressed with collinearity equation:
In formula, (xc, yc) is the focal plane coordinate of picture point, and f is focal length, and (X, Y, Z) is that corresponding object space point is sat admittedly in star
The coordinate of system is marked, (Xs, Ys, Zs) is the coordinate that photo centre consolidates coordinate system in star, and the referred to as line element of elements of exterior orientation, λ is
One scale factor, R is the spin matrix that image space coordinate system consolidates coordinate system to star, by three exterior orientation angle elements
Composition.
(b) reading of initial elements of exterior orientation
Outer orientation is carried out to image, it is necessary first to obtain the elements of exterior orientation of imaging moment.Elements of exterior orientation is from track
It measures and is obtained in obtained position of aircraft and attitude data, these data measured are stored in LRO NAC as auxiliary data
In the SPICE kernel file of image, read so the elements of exterior orientation of every image can correspond in SPICE kernel from it
It takes.
(c) elements of exterior orientation of every scan line of interpolation
For the orbiter, orbital vehicle image of push-broom type imaging, each scan line has corresponding elements of exterior orientation.But satellite
Orbit measurement time interval is greater than each row image scan imaging time interval, to obtain the elements of exterior orientation needs of every scan line
By the way of interpolation.The general elements of exterior orientation function established using three rank multinomials relative to imaging time t, according to record
Every row CCD imaging time, the elements of exterior orientation of every scan line can be obtained with interpolation.
In formula, Xs(t),Ys(t),Zs(t) indicate that t moment photo centre consolidates the coordinate in coordinate system, i.e. foreign side's bit line in star
Element；Indicate that the attitude angle in coordinate system, i.e. exterior orientation angle element are consolidated in star in t moment focal plane；aX 0……aκ 3Indicate that least square method can be used to solve according to orbital measurement data for the multinomial coefficient of corresponding parameter, these coefficients.
(d) it by collinearity equation and the elements of exterior orientation acquired, can will be converted by the focal plane coordinate of distortion correction
At object coordinates, the foundation of the stringent imaging geometry model of sensor is completed.
1.2) foundation of LRO NAC rational function model
The foundation of LRO NAC rational function model needs to initially set up virtual controlling grid, further according to the virtual control of generation
System point solves rational function model parameter.
1.2.1) the foundation of virtual controlling grid
When establishing virtual controlling grid, need the elevation of imagery zone to be divided into several elevation faces, in image space with certain
Spacing generate the grid points coordinate of image as image space virtual controlling point, then according to rigorous geometric model by grid points image
Object space virtual controlling point is obtained on coordinate projection to each elevation face.
1.2.2) the solution of rational function model parameter
Rational polynominal model sets up any topocentric coordinates (lat, lon, h) and corresponding image by ratio multinomial
One-to-one relationship between coordinate (sample, line), expression-form are as follows:
In formula,
NumL(P, L, H)=a1+a2L+a3P+a4H+a5LP+a6LH+a7PH+a8L2+a9P2
+a10H2+a11PLH+a12L3+a13LP2+a14LH2+a15L2P+a16P3+a17PH2
+a18L2H+a19P2H+a20H3
DenL(P, L, H)=b1+b2L+b3P+b4H+b5LP+b6LH+b7PH+b8L2+b9P2
+b10H2+b11PLH+b12L3+b13LP2+b14LH2+b15L2P+b16P3+b17PH2
+b18L2H+b19P2H+b20H3
Nums(P, L, H)=c1+c2L+c3P+c4H+c5LP+c6LH+c7PH+c8L2+c9P2
+c10H2+c11PLH+c12L3+c13LP2+c14LH2+c15L2P+c16P3+c17PH2
+c18L2H+c19P2H+c20H3
Dens(P, L, H)=d1+d2L+d3P+d4H+d5LP+d6LH+d7PH+d8L2+d9P2
+d10H2+d11PLH+d12L3+d13LP2+d14LH2+d15L2P+d16P3+d17PH2
+d18L2H+d19P2H+d20H3
Wherein, ai,bi,ci,di(i=1~20) are rational function model parameter, b1And d1Usually 1, (P, L, H) is to return
One ground coordinate changed, (X, Y) are normalized image coordinate,
Normalization mode is as follows:
In formula, LINE_SCALE, SAMP_SCALE, SAMP_OFF and LINE_OFF are the normalized parameter of image space coordinate；
LAT_OFF, LON_OFF, HEIGHT_OFF, LAT_SCALE, LON_SCALE, HEIGHT_SCALE are object coordinates normalization ginseng
Number, lat is latitude, and lon is longitude, and h is elevation.
1.2.3) as 1.2.1) obtained in virtual controlling point, pass through least square and solve 78 rational function models ginseng
Number, can establish the rational function model of every image.
2, remote sensing image and dem data to be matched are read, DEM and remote sensing image data subject to registration are matched.DEM with
There are many matching ways of image, herein using the matching process of the DEM and image constrained jointly using geometry and radiation information
It is illustrated, but be not restricted to that this method, method detailed process are as follows:
2.1) DEM is generated into analog image, as reference images.
2.1.1) on DEM, each pixel is calculated pixel-by-pixel in the gradient of x and the direction y:
In formula, e1, e2…e9For centered on DEM pixel calculated, corresponding each pixel in 3 × 3 window
Value, is illustrated in figure 23 × 3 window schematic diagram.
2.1.2) according to gradient value, the gradient, slope aspect value are calculated pixel-by-pixel:
A=p/q (8)
In formula, S is value of slope, and A is slope aspect value.
2.1.3) according to the header file information of remote sensing image data, solar azimuth when remote sensing image obtains is obtained
(Azi) with solar elevation (Alt), the empty image with DEM same size is created, calculates its corresponding simulation radiation value pixel-by-pixel
Information, and it is stored in empty image, building obtains reference images.
I=255 × (cosAlt × cosS+sinAlt × sinS × cosAzi-A) (9)
In formula, I is the simulation radiation value for each pixel of simulation reference images being calculated, when Alt is that remote sensing image obtains
Solar elevation, Azi be remote sensing image obtain when solar azimuth.
2.2) affine-Scale invariant features transform (ASIFT) algorithm is utilized, reference images and remote sensing image initial are obtained
With point.
Affine-Scale invariant features transform (ASIFT) algorithm is the algorithms most in use in remote sensing technology field, which can obtain
The initial matching point between remote sensing image and reference images is obtained, specifically:
The sample sequence for obtaining video camera longitude angle and angle of latitude first, all may be made for simulating by camera optical axis
At affine deformation come realize image convert；Then remote sensing image to be matched is subjected to Sloped rotating transformation and generates simulation shadow
Picture；The analog image of generation is finally subjected to characteristic point detection and reference images according to Scale invariant features transform (SIFT) algorithm
Matching, the final initial matching point obtained between remote sensing image and reference images.
2.3) initial matching point is utilized, rational function model is calculated and initially refines parameter.
The present invention corrects back projection's error using the affine transformation of image space, and introducing is refined, and parameter (join by column parameter and row
Number) above-mentioned error is corrected:
In formula, (r, c) is picpointed coordinate, and (r ', c ') is projection coordinate of the ground point using RFM back projection to image space,It refines parameter for RFM.
2.4) the matched initial value of least square essence is obtained.
2.4.1 feature point extraction operator) is utilized, such as Harris, Forstner etc. extracts feature in reference images
Point.
2.4.2) using the rational function model refined, the characteristic point in reference images is calculated onto remote sensing image, is made
For Least squares matching initial value.
The rational function model and step 2.3) that the characteristic point extracted in step 2.4.1) is obtained by step 1 calculate
The parameter back projection that refines obtained obtains picpointed coordinate of the characteristic point on image, matches as total least squares to image space
Initial value:
(a) according to the header file information of reference images, step 2.4.1) the reference images characteristic point extracted is calculated to big
Ground coordinate:
Y=Y0+fy×Ry
X=X0+fx×Rx (11)
In formula, Y and X is respectively the latitude and longitude coordinate of characteristic point, Y0With X0The respectively latitude of reference images upper left angle point
Degree and longitude coordinate, fy, fxIt is characterized the ranks number a little in reference images, Ry, RxIt is benchmark image in the direction y and the direction x
Resolution ratio.
According to the latitude and longitude coordinates being calculated, the height value Z of corresponding longitude and latitude is extracted on digital elevation model.
(b) RPC being calculated according to step 1 and step 2.3) and refine parameter and formula (4), formula (10) lead to
It crosses (X, Y, Z) and picpointed coordinate (r, c) of the characteristic point on correspondence remote sensing image subject to registration is calculated；
2.5) based on the Least squares matching of global restriction, export remote sensing image to be matched radiation deformation parameter and
Geometry deformation parameter, detailed process are as follows:
2.5.1 it) is solving remote sensing image and while reference images registration parameter, is resolving rational function model and refine ginseng
It is as follows to introduce global restriction Least squares matching model for number:
In formula, g1With g2For the radiation value function of benchmark image and remote sensing image tie point respective coordinates position subject to registration, n1
With n2For the function of benchmark image and remote sensing image noise subject to registration, (x, y) is coordinate of the tie point in reference images, h0, h1
Radiation deformation parameter between benchmark image and remote sensing image, ar 0……ar 2,bc 0……bc 2For geometry deformation parameter (ginseng of refining
Number), (r ', c ') is projection coordinate of the ground point using RFM back projection to image space, and (r, c) is that the projection of back projection to image space is sat
The picpointed coordinate that mark obtains after refining, δr,δcFor model error.
2.5.2) as follows according to global restriction Least squares matching model foundation error equation:
In formula, vgFor the error of equation radiation value between being registrated image, vrError for equation in column direction, vcFor equation
In the error of line direction.
First order Taylor expansion is carried out to formula (13), the error equation of available linearisation:
In formula,For 0 item of Taylor series,Respectively indicate the single order of column direction Yu line direction coordinate
Partial derivative.
At the beginning of input is the Least squares matching that step 2.2) is calculated in solving match parameter and parametric procedure of refining
Value, and using initial value point as all the points in the window of center n × n size, these are put into point as input, herein n value
It is 9.For the point of all inputs, error equation is established using linearized stability equation (14), step 2.2) is calculated
Initial value point out establishes error equation using linearized stability equation (15), finally carries out to all error equations of foundation
Unified Solution show that match parameter and rational function model are refined parameter.
2.5.3) to the matching initial value point of each input according to the error equation in formula (15), node-by-node algorithm normal equation.
2.5.4) normal equation is solved, and unknown number is corrected, and judge the related coefficient being calculated after adjustment,
Such as the sum of all Point correlation coefficients meets the requirements (sum of the sum of this related coefficient less than the related coefficient of last iteration),
Adjustment result is then exported, if adjustment precision is unsatisfactory for requiring, then repeatedly step 2.5.2), 2.5.3) and 2.5.4), iterative solution,
Wherein, correlation coefficient ρ2:
2.5.5 the radiation deformation parameter and geometry deformation parameter for) exporting remote sensing image, complete remote sensing image and benchmark shadow
Total least squares matching as between.
3, remote sensing image ortho-rectification
It, can using the video imaging geometrical model established in the geometry deformation parameter and step 1 solved in step 2
To carry out ortho-rectification to every image, corresponding DOM product is obtained.Image ortho-rectification is generally divided into positive solution and anti-solution
Method, the present invention describe specific implementation process by taking anti-solution as an example:
3.1) topocentric coordinates are calculated.
Setting will generate the X-direction and Y-direction resolution ratio (R of orthography firstxdom, Rydom), which can basis
Sets itself is needed, can also be configured according to the original resolution of remote sensing image.Meanwhile utilizing raw video size and shadow
The geometrical model of picture calculates the coverage area of image.Upper left angular coordinate (the X of image is set according to the range of image0,Y0).If
The picpointed coordinate of any point pixel center P is (x on pre-generatmg DOMdom,ydom), calculate the corresponding ground coordinate (X, Y) of P point；
X=X0+Rxdom*xdom
Y=Y0+Rydom*ydom
3.2) picpointed coordinate is calculated
(X, Y) and DEM, interpolation is utilized to go out the elevation Z of the point, using the video imaging geometrical model after establishing and refining,
Calculate corresponding picpointed coordinate p (x, y) on original image；
X=f1 (X, Y, Z)
Y=f2 (X, Y, Z)
In formula, f1 and f2 represent the video imaging geometrical model after refining, and Z indicates the elevation of P point.
3.3) gray scale interpolation
It, thus must be into since obtained picpointed coordinate p (x, y) not necessarily falls in the pixel center of raw video
Row gray scale interpolation, generally can be used bilinear interpolation, acquire the gray value g (x, y) of picture point p；
3.4) gray scale assignment
The gray value of picture point p is assigned to the P point on pixel namely DOM after correcting；
3.5) above-mentioned operation successively is completed to pixel each on DOM, the DOM product by topographical correction can be obtained.
4, adjustment is just penetrated based on thin plate spline (Thin Plate Spline, TPS) model: is obtained just according in step 3
Positional relationship between projection picture determines the overlapping region between image, matches to obtain same place in overlapping region, then same to every group
Famous cake establishes the error approach based on TPS model, the reduction of geometrical deviation between image is obtained by iterative solution, to orthogonal projection
It refines as carrying out further geometric position, so that achieving the effect that seamless, detailed process between image are as follows:
4.1) according to the range information for obtaining orthography in step 3, the overlapping region between image is determined, wherein image
Overlay region be geographical coordinate rectangular extent intersection.Least squares matching, SIFT, SURF, A-SIFT are utilized in overlapping region
Equal matching process obtain same place.
4.2) its upper left angular coordinate and resolution ratio are read from DOM header file, according to formula (11) will matching obtain it is same
Famous cake coordinate solution is asked to obtain ground coordinate:
4.3) it establishes and adjustment error equation is just penetrated based on TPS model, and solved using the principle of least square, obtained
Geometry between orthography is refined parameter, specifically:
4.3.1 the model of TPS) is established:
In formula, ax 0,ax 1,ax 2,by 0,by 1,by 2,δi(i=1,2 ... n) be TPS model parameter, x, y are original coordinates,
X', y' are the coordinate after correction, and n is the quantity of same place, ψ (ri) it is radial basis function:
In formula, r is any point in image to the Euclidean distance of matching same place.
4.3.2 the same place of error equation) based on TPS model for each Xie Qiuzhi object space, wherein be based on TPS mould
The error equation of type is as follows:
In formula,The respectively coordinate residual error in ranks direction,For original object coordinates,
For the object coordinates after refining, i indicates that piont mark, j indicate video number, and n indicates the sum of the same place on the image.
It is expressed as follows with matrix form:
V=Ax-L
L=[Dx1 Dy1]T
4.3.3) for point range Liru of the same name each of on every image shown on error equation, according to least square original
Reason solves and obtains the coefficient of TPS model.
4.4) obtained TPS model is solved according to 4.3) middle, correction and resampling, solution procedure is carried out to every image
It is similar with the process of orthography is generated in step 3, positive solution and opposition method can also be divided into, said by taking positive solution as an example here
It is bright:
4.4.1 the upper left angle point ground coordinate and resolution ratio of orthography after correcting) are determined, before generally defaulting and correct
The resolution ratio of image is identical, and upper left angular coordinate is converted to obtain by the former upper left DOM angular coordinate according to TPS model parameter:
In formula, (X0,Y0) it is original orthography upper left angle point ground coordinate, it can be read from the header file of DOM
It arrives, (X0',Y0') be correct after orthography upper left angle point ground coordinate.
4.4.2) for every bit (x on original orthographydom,ydom), it will according to formula (11) using its header file information
It is converted into ground coordinate (X, Y).
4.4.3 coordinate adjustment) is carried out to X, Y, the coordinate X', Y' after obtaining its correction；
4.4.4 image coordinate (the x of DOM after) converting the ground coordinate after correction to correctiondom',ydom'):
xdom'=(X'-X0')/mx'
ydom'=(Y'-Y0')/my'
In formula, (mx',my') be correct after DOM resolution ratio, (X0’,Y0') sat for the upper left angle point ground of DOM after correction
Mark.
4.4.5) by (xdom,ydom) gray value be assigned to (xdom',ydom'), above-mentioned correction procedure is completed pixel-by-pixel, finally
Orthography after gray scale resampling is corrected is carried out to DOM.
5, it the even light of carry out to orthography after all corrections and inlays, obtains the orthophotomap product in big region.
The map products can be used for topography and geomorphology analysis, Objects extraction and control benchmark of low resolution imagery etc..
According to the disclosure and teachings of the above specification, those skilled in the art in the invention can also be to above-mentioned embodiment party
Formula carries out change and modification appropriate.Therefore, the invention is not limited to the specific embodiments disclosed and described above, to this
Some modifications and changes of invention should also be as falling into the scope of the claims of the present invention.In addition, although this specification
In use some specific terms, these terms are merely for convenience of description, does not limit the present invention in any way.
Claims (10)
1. a kind of seamless orthography production method in the big region of satellite remote-sensing image, which is characterized in that including the following contents:
1) remote sensing image imaging geometry model is constructed；
2) remote sensing image and dem data to be matched are read, DEM and remote sensing image data subject to registration are matched；
3) remote sensing image ortho-rectification obtains corresponding DOM product；
4) according to the positional relationship obtained between orthography in step 3), the overlapping region between image is determined, in overlapping region
With obtaining same place, the error equation based on TPS model is established to every group of same place, geometry between image is obtained by iterative solution
The reduction of deviation；
5) even light is carried out to orthography after all corrections and inlayed, obtain the orthophotomap product in big region.
2. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 1, which is characterized in that right
DEM and remote sensing image data subject to registration are carried out matching and are matched with the DEM that radiation information constrains jointly with image using geometry,
Detailed process are as follows:
2.1) DEM is generated into analog image, as reference images；
2.2) affine-Scale invariant features transform algorithm is utilized, reference images and remote sensing image initial matching point are obtained；
2.3) initial matching point is utilized, rational function model is calculated and initially refines parameter；
2.4) the matched initial value of least square essence is obtained；
2.5) based on the Least squares matching of global restriction, the radiation deformation parameter and geometry of remote sensing image to be matched are exported
Deformation parameter.
3. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 2, which is characterized in that on
It states step 2.2) and utilizes affine-Scale invariant features transform algorithm, obtain reference images and remote sensing image initial matching point, specifically
Process are as follows:
First obtain video camera longitude angle and angle of latitude sample sequence, for simulate it is all may be as caused by camera optical axis
Affine deformation come realize image convert；
Then remote sensing image to be matched is subjected to Sloped rotating transformation and generates analog image；
The analog image of generation is finally subjected to characteristic point detection according to Scale invariant features transform algorithm and reference images match,
Obtain the initial matching point between remote sensing image and reference images.
4. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 2, which is characterized in that on
State the detailed process that step 2.4) obtains the matched initial value of least square essence are as follows:
2.4.1 characteristic point) is extracted in reference images using feature point extraction operator；
2.4.2) using the rational function model refined, the characteristic point in reference images is calculated onto remote sensing image, as most
Small two multiply matching initial value.
5. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 2, which is characterized in that on
Least squares matching of the step 2.5) based on global restriction is stated, the radiation deformation parameter of remote sensing image to be matched and several is exported
What deformation parameter, detailed process are as follows:
2.5.1 it) is solving remote sensing image and while reference images registration parameter, is resolving rational function model and refine parameter, draw
It is as follows to enter global restriction Least squares matching model:
g1(x,y)+n1(x, y)=h0+h1g2(r,c)+n2(x,y)
R=ar 0+ar 1r′+ar 2c′+δr
C=bc 0+bc 1r′+bc 2c′+δc
In formula, g1With g2For the radiation value function of benchmark image and remote sensing image tie point respective coordinates position subject to registration, n1With n2
For the function of benchmark image and remote sensing image noise subject to registration, (x, y) is coordinate of the tie point in reference images, h0, h1For base
Radiation deformation parameter between quasi- image and remote sensing image, ar 0……ar 2,bc 0……bc 2For geometry deformation parameter (parameter of refining),
(r ', c ') is projection coordinate of the ground point using RFM back projection to image space, and (r, c) is projection coordinate essence of the back projection to image space
The picpointed coordinate obtained after change, δr,δcFor model error；
2.5.2) according to global restriction Least squares matching model foundation error equation:
vg=h0+h1g2(r,c)-g1(x,y)
vr=ar 0+ar 1r′+ar 2c′+δr-r
vc=bc 0+bc 1r′+bc 2c′+δc-c
In formula, vgFor the error of equation radiation value between being registrated image, vrError for equation in column direction, vcIt is expert at for equation
The error in direction；
First order Taylor expansion is carried out to error equation, the error equation linearized:
In formula,For 0 item of Taylor series,Respectively indicate the single order local derviation of column direction Yu line direction coordinate
Number；
2.5.3) to the matching initial value point of each input according to error equation, node-by-node algorithm normal equation；
2.5.4 normal equation) is solved, unknown number is corrected, and judge the related coefficient being calculated after adjustment, if all
The sum of Point correlation coefficient is met the requirements, then exports adjustment result, otherwise enters step 2.5.2 iterative solution；
2.5.5 the radiation deformation parameter and geometry deformation parameter for) exporting remote sensing image, are completed between remote sensing image and reference images
Total least squares matching.
6. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 1, which is characterized in that on
Step 3) remote sensing image ortho-rectification is stated using positive solution and anti-solution.
7. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 6, which is characterized in that adopt
With anti-solution to the detailed process of remote sensing image ortho-rectification are as follows:
3.1) setting will generate the X-direction and Y-direction resolution ratio (R of orthographyxdom, Rydom), utilize raw video size and shadow
The geometrical model of picture calculates the coverage area of image, and the upper left angular coordinate (X of image is arranged according to the range of image0,Y0), if
The picpointed coordinate of any point pixel center P is (x on pre-generatmg DOMdom,ydom), calculate the corresponding ground coordinate (X, Y) of P point；
X=X0+Rxdom*xdom
Y=Y0+Rydom*ydom
3.2) (X, Y) and DEM, interpolation is utilized to go out the elevation Z of the point, using the video imaging geometrical model after establishing and refining,
Calculate corresponding picpointed coordinate p (x, y) on original image；
X=f1 (X, Y, Z)
Y=f2 (X, Y, Z)
In formula, f1 and f2 represent the video imaging geometrical model after refining, and Z indicates the elevation of P point；
3.3) gray scale interpolation acquires the gray value g (x, y) of picture point p；
3.4) the P point being assigned to the gray value of picture point p on pixel namely DOM after correcting；
3.5) above-mentioned operation successively is completed to pixel each on DOM, the DOM product by topographical correction can be obtained.
8. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 1, which is characterized in that on
Step 4) is stated according to the positional relationship obtained between orthography in step 3), the overlapping region between image is determined, in overlapping region
Matching obtains same place, then establishes the error approach based on TPS model to every group of same place, obtains image by iterative solution
Between geometrical deviation reduction, detailed process are as follows:
4.1) according to the range information for obtaining orthography in step 3), the overlapping region between image is determined, wherein the weight of image
Folded area is the intersection of geographical coordinate rectangular extent, obtains same place in overlapping region；
4.2) its upper left angular coordinate and resolution ratio are read from DOM header file, and the identical point coordinates solution that matching obtains is acquired
To ground coordinate:
4.3) it establishes and adjustment error equation is just penetrated based on TPS model, and solved using the principle of least square, just penetrated
Geometry between image is refined parameter.
9. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 8, which is characterized in that on
It states step 4.3) foundation and adjustment error equation is just penetrated based on TPS model, and solved using the principle of least square, obtained just
Geometry between projection picture is refined parameter, detailed process are as follows:
4.3.1 the model of TPS) is established:
In formula, ax 0,ax 1,ax 2,by 0,by 1,by 2,δi(i=1,2 ... n) be TPS model parameter, x, y are original coordinates, and x', y' are
Coordinate after correction, n are the quantity of same place, ψ (ri) it is radial basis function:
In formula, r is any point in image to the Euclidean distance of matching same place；
4.3.2 the same place of error equation) based on TPS model for each Xie Qiuzhi object space, wherein based on TPS model
Error equation:
In formula,The respectively coordinate residual error in ranks direction,For original object coordinates,For essence
Object coordinates after change, i indicate that piont mark, j indicate video number, and n indicates the sum of the same place on the image；
It is expressed as follows with matrix form:
V=Ax-L
L=[Dx1 Dy1]T
4.3.3) for point range Liru of the same name each of on every image shown on error equation, asked according to the principle of least square
Solution obtains the coefficient of TPS model；
4.4) obtained TPS model is solved according to 4.3) middle, correction and resampling is carried out to every image, after being corrected just
Projection picture.
10. the seamless orthography production method in the big region of satellite remote-sensing image according to claim 9, which is characterized in that
Above-mentioned steps 4.4) every image is corrected and resampling is using positive solution or anti-solution；Wherein, every image is carried out
Correct the detailed process that positive solution is used with resampling are as follows:
4.4.1 the upper left angle point ground coordinate and resolution ratio of orthography after correcting) are determined:
In formula, (X0,Y0) it is original orthography upper left angle point ground coordinate, (X0',Y0') be correct after orthography upper left
Angle point ground coordinate；
4.4.2) for every bit (x on original orthographydom,ydom), convert thereof into ground coordinate (X, Y).
4.4.3 coordinates correction) is carried out to X, Y, the coordinate X', Y' after obtaining its correction；
4.4.4 image coordinate (the x of DOM after) converting the ground coordinate after correction to correctiondom',ydom'):
xdom'=(X'-X0')/mx'
ydom'=(Y'-Y0')/my'
In formula, (mx',my') be correct after DOM resolution ratio, (X0’,Y0') be correct after DOM upper left angle point ground coordinate；
4.4.5) by (xdom,ydom) gray value be assigned to (xdom',ydom'), above-mentioned correction course is completed pixel-by-pixel, finally to DOM
Carry out the orthography after gray scale resampling is corrected.
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