CN103679711B  A kind of remote sensing satellite linear array push sweeps optics camera outer orientation parameter calibration method inorbit  Google Patents
A kind of remote sensing satellite linear array push sweeps optics camera outer orientation parameter calibration method inorbit Download PDFInfo
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 CN103679711B CN103679711B CN201310632138.9A CN201310632138A CN103679711B CN 103679711 B CN103679711 B CN 103679711B CN 201310632138 A CN201310632138 A CN 201310632138A CN 103679711 B CN103679711 B CN 103679711B
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Abstract
Description
Technical field
The present invention relates to a kind of remote sensing satellite linear array pushbroom type optics camera outer orientation parameter calibration method inorbit, belong to remote sensing satellite image processing technology field, for the optimization of the positioning precision inorbit lifting of the linear pushbroom type camera that remote sensing satellite carries.
Background technology
Along with the continuous transmitting of domestic and international remote sensing satellite, the resolving power of remote sensing satellite improves constantly, and the features such as the big area of satellite remote sensing, short period, low cost become the dominant direction of remote sensing development. Ended for the end of the year 2012, China has transmitted multiseries many Optical remote satellites such as environment, resource, remote sensing, but a large amount of domestic remotelysensed datas is not fully used, and traces it to its cause, geometric positioning accuracy is low is the important factor of subsequent applications effect of a restriction remotelysensed data. External commercial optical remote sensing satellite geometric positioning accuracy reaches the precision of rice level, and such as Geoeye, WorldView etc., and in the remote sensing satellite that China has launched, resource 02 star positioning precision is 7Km, resource No. two 03 star positioning precision 200m. Domestic remote sensing satellite is launch the Accurate Calibration that camera is not carried out outer orientation element by rear ground without a major reason of control positioning precision difference.
Linear array pushbroom type optics camera is the main flow of current Optical remote satellite sensor, and linear array pushbroom type optics camera pushes ahead dynamic imaging with satellite along the track predefined, and forms a width bidimensional image one by one after scanning. In certain photography moment, row corresponding on image with taken the photograph ground and be there is strict central projection relation. By ground survey error, the impact launching the factors such as rear environment impulse force, the outer orientation parameter after remote sensing satellite transmitting changes so that it seriously declines without control geometric positioning accuracy, lacks the direct demarcation means to satellite in orbit outer orientation element in floor treatment.
Summary of the invention
The technical problem that the present invention solves is: overcome the deficiencies in the prior art, a kind of remote sensing satellite linear array push is provided to sweep optics camera outer orientation parameter calibration method inorbit, utilize a reference mark, scape image collection accurate ground, the rigorous geometry model of camera can be revised, resolve accurate outer orientation parameter, can be used for promoting the nothing control geometric positioning accuracy that camera obtains image, support the geometry calibration of remote sensing satellite linear array pushbroom type optics camera.
The technical scheme of the present invention is: a kind of remote sensing satellite linear array push sweeps optics camera outer orientation parameter calibration method inorbit, comprises the following steps:
1) calibration image I is being treated_{1}, control image I_{2}On utilize the automatic matching algorithm in reference mark, gather reference mark information, described reference mark information is that T registering control points is to { PixI_{1i}, PixI_{2i}, wherein i=1,2,3 ..., T); Record each dominating pair of vertices { PixI_{1i}, PixI_{2i}Treat calibration image I_{1}Picture point coordinate (i, j) and the corresponding control image unique point position coordinate (lat, lon, height) under WGS84 system of coordinates;
2) camera internal position element and the auxiliary data at each reference mark place is obtained, when described auxiliary data comprises the track of satellite, attitude, row; Set up the rigorous geometry model at each reference mark place
3) according to step 2) rigorous geometry model that obtains, calculate the positioning error size and direction that obtain each reference mark place, preliminary excluding gross error reference mark, obtains initial control point pair;
4) camera outer orientation element geometry calibration mathematical model is set up, according to step 3) initial control point pair that obtains, calculate and obtain each reference mark process opinion pointing vector and actual pointing vector;
41) in rigorous geometry model, introduce error matrix M, set up camera outer orientation element geometry calibration mathematical model
42) to arbitrary group of reference mark, element (psiX, psiY) calculation control point place, the interior orientation optical axis at reference mark place is utilized to point to vector V in camera coordinates system, as the actual pointing vector V at this reference mark place after being normalized_{Actual};
43) to arbitrary group of reference mark, utilize reference mark ground point location coordinate (lat, lon, height) calculation control point place optical axis points to vector V in camera coordinates system, as the theoretical pointing vector V at this reference mark place after being normalized_{Theoretical};
Wherein
5) set up three axle rotation error equations between theoretical pointing vector and actual pointing vector, this three axles rotation error equation is carried out linearization process, obtains system of linear equations;
51) set error matrix M as three axle rotating orthogonal matrixes, set up the matrix conversion model between theoretical pointing vector and actual pointing vector:
Wherein
52) model is changed, obtain the nonlinear equation of the theoretical each parameter of pointing vector with each parameter of actual pointing vector;
53) nonlinear equation is carried out Taylor expansion at initial point (0,0,0) place, obtain the linearapporximation equation of nonlinear equation;
54) for each group of theoretical pointing vector and actual pointing vector, calculate linear equation parameter, obtain system of linear equations;
6) method of least squares iteration process of solution 5 is adopted) system of linear equations that obtains, obtain and demarcate outer parameter.
Step 1) in gather reference mark information concrete grammar be:
11) calibration original image I is treated based on SIFT algorithm_{1}Carry out feature point extraction, obtain M unique point PixI_{1i}(i=1,2,3 ..., M), M is positive integer; Record the SIFT feature vector of each unique point;
12) based on SIFT algorithm, high precision is controlled image I_{2}Carry out feature point extraction, obtain N number of unique point PixI_{2i}(i=1,2,3 ..., N), N is positive integer; Record the SIFT feature vector at each unique point place;
13) adopt Europe formula distance the unique point of two width images to be mated as similarity measurement criterion, obtain T registering control points to { PixI_{1i}, PixI_{2i}(i=1,2,3 ..., T).
Step 2) in set up each reference mark place the concrete grammar of rigorous geometry model be:
21) according to dominating pair of vertices { PixI_{1i}, PixI_{2i}Image coordinate (i, j), when obtaining the row of its correspondence;
22) Lagrange's interpolation algorithm interpolation calculation dominating pair of vertices { PixI is utilized_{1i}, PixI_{2i}Corresponding row time track position (PX, PY, PZ, VX, VY, VZ);
23) attitude quaternion passed down by satellite carries out ordinate transform process, utilizes Lagrange algorithm interpolation calculation dominating pair of vertices { PixI_{1i}, PixI_{2i}Corresponding row time moment place's camera relative to the threeaxis attitude angle (Roll, Pitch, Yaw) of track system of coordinates;
24) according to camera laboratory measurements or interior orientation element geometry calibration result, calculation control point is to { PixI_{1i}, PixI_{2i}Corresponding CCD visits unit's optical axis and points to angle (psiX, psiY);
25) according to step 21)step 24) result that obtains, set up the rigorous geometry model at reference mark place, for certain on the image that the linear array pushscanning image camera a certain moment obtains a bit, build the rigorous geometry model of remote sensing image;
Step 3) in the concrete acquisition methods of initial control point pair be:
31) according to step 2) rigorous geometry model that obtains, calculate each dominating pair of vertices { PixI_{1i}, PixI_{2i}In treat calibration imaging point PixI_{1i}Initial ground point location coordinate { lon ', lat ' };
32) according to each dominating pair of vertices { PixI_{1i}, PixI_{2i}Middle control imaging point PixI_{2i}Location coordinate information, calculation control point PixI_{1i}Initial fix error { �� xi, �� yi};
33) calculation control point is to { PixI_{1i}, PixI_{2i}Positioning error mean value { �� x, �� y};
34) to each dominating pair of vertices, judge its initial fix error �� xi, �� yi} whether �� x �� 20% �� x, within the scope of �� y �� 20% �� y}, will not { dominating pair of vertices within the scope of �� x �� 20% �� x, �� y �� 20% �� y} be rejected;
35) reject error control point to rear, obtain K group initial control point to { PixI_{1i}, PixI_{2i}(i=1,2,3 ..., K).
The present invention's useful effect compared with prior art is:
(1) remote sensing satellite linear array pushbroom type optics camera rigorous geometry model and auxiliary data characteristic are conducted indepth analysis by the present invention, the auxiliary data optimization process such as devise when comprising the information acquisition of reference mark, image highprecision ground, track camera, attitude, row, parameter calibration flow process outside strict geometry calibration mathematical model is set up, desirable pointing vector calculates with actual pointing vector, triaxial error establishing equation and equation linearization process, least square resolve the steps such as outer orientation parameter high precision, meet satellite geometry calibration demand inorbit.
(2) in the information extraction of reference mark, high precision ground, for features such as the rotation of basic image and highprecision control point image, yardsticks, have employed the Control point extraction based on SIFT algorithm and coupling, it is to increase the accuracy of Control point extraction.
(3) in whole calibration process, it is contemplated that faulttolerant based on the initial control point of rigorous geometry model, registering control points to screening, is ensured to gather the exactness of dominating pair of vertices by the principle that utilizes image each point positioning error basically identical.
(4) when setting up geometry calibration mathematical model, introduce three axle rotating orthogonal matrixes as error matrix, after error equation linearizing, the basis reducing the difficulty that error equation resolves well ensures that linearized stability equation is to the matching similarity of true error.
(5) have employed the iteration error based on least square when resolving outer orientation parameter and reject design, it is possible to further excluding gross error reference mark, it is to increase the precision of parameter calibration.
Accompanying drawing explanation
Fig. 1 is the inventive method schema.
Embodiment
Below in conjunction with accompanying drawing 1, the specific embodiment of the present invention is further described in detail:
1. treating calibration image I_{1}, high precision control image I_{2}On utilize the automatic matching algorithm in reference mark to gather highprecision control point to information,
(1) calibration original image I is treated based on SIFT algorithm_{1}Carry out feature point extraction, obtain m unique point PixI_{1i}(i=1,2 ..., m), record the SIFT feature vector of each unique point;
SIFT feature matching algorithm characterizes as follows:
(1.1) characteristic point position coordinate and place yardstick is determined. Set up image gaussian pyramid, 26 neighborhoods in pyramid scale space detect extreme value, if certain point (x, y) is maximum in 26 neighborhoods of this layer of pyramid scale space and upper and lower two layers or during minimum value, defining this point is the unique point of image under this yardstick L.
(1.2) unique point direction parameter calculates. Sample in the neighborhood window centered by unique point (x, y), with the gradient direction of statistics with histogram neighborhood territory pixel. The scope of histogram of gradients is divided into 36 directions, and every 10 degree represent a direction. The direction at definition histogram peak place is the direction parameter of this unique point.
(1.3) SIFT feature vector description is calculated. Around unique point center, get the window of 8*8, window is cut into the subwindow of 2*2. Every subwindow calculates the gradient orientation histogram in 8 directions, adds up the cumulative value in each direction, as the directional information of this subwindow. 4 subwindows are the final 32 dimensional characteristics vectors generating this unique point place after having added up.
(2) based on SIFT algorithm, high precision is controlled image I_{2}Carry out feature point extraction, obtain n unique point PixI_{2i}(i=1,2 ..., n), record the SIFT feature vector at each unique point place;
(3) adopt Europe formula distance the unique point of two width images to be mated as similarity measurement criterion, obtain M registering control points to { PixI_{1i}, PixI_{2i}(i=1,2 ..., M);
(3.1) for treating calibration original image I_{1}Arbitrary reference mark PixI_{1i}, calculate its SIFT feature vector and high precision control image I_{2}Europe formula distance between n the proper vector that upper extraction obtains. For two vectorial l_{1}(x_{1},x_{2},��,x_{n})��l_{2}(y_{1},y_{2},��,y_{n}), its Europe formula distance characterizes as follows:
(3.2) minimum value in m Europe formula distance is calculated, the unique point PixI at minimum value place_{2j}It is and treats calibration original image I_{1}Unique point PixI_{1i}Matching characteristic point.
(4) to each matching characteristic point to { PixI_{1i}, PixI_{2j}, calibration original image I treated in record_{1}Unique point PixI_{1i}Place's picture point planimetric coordinates (sample, line), and high precision control image I_{2}Unique point PixI_{2j}Place's threedimensional location coordinates (lat, lon, height), as the highprecision control point information collected.
2. the auxiliary data such as when obtaining interior orientation, place, each reference mark element, track, attitude, row, sets up the rigorous geometry model at each reference mark place.
(1) data such as when resolving track in calibration video imaging time range, attitude, row in the raw data passed from satellite;
(2) to arbitrary dominating pair of vertices Points_{i}{ sample, line, lat, lon, height}, according to the image coordinate (sample, line) of dominating pair of vertices, obtain the photography time scanTime of its correspondence;
Directly resolving in the auxiliary data that photography time corresponding to reference mark can pass from satellite, imaging time corresponding to the capable auxiliary data of line is photography time corresponding to this reference mark.
(3) satellite orbital position (PX, PY, PZ, VX, VY, VZ) of Lagrange's interpolation algorithm interpolation calculation photography time scanTime is utilized; Satellite passes orbital data according under certain frequency, and therefore, before and after the satellite orbital position needs utilization photography moment that photography time scanTime is corresponding, the orbital data of several groups carries out interpolation calculation. The present invention adopts Lagrange's interpolation algorithm, utilizes the satellite orbital position of three groups of orbital data calculating photography times before and after photography time.
(3.1) from first group of orbital data, judge the relation between the generation time of this group orbital data, the generation time of next group orbital data and scanTime, if scanTime is greater than the generation time of ith group of orbital data, the generation time being simultaneously less than the ith+1 group orbital data, then recording i is the distance nearest orbital data sequence number of photography time.
(3.2) utilize the ith1, i, i+1 group orbital data, calculate track position and the speed in photography moment based on Lagrange algorithm. Lagrange's interpolation algorithm is expressed as follows:
For the function table (x of known y=f (x)_{i},f(x_{i})) (i=0,1 ..., n), at [x_{o},x_{n}] arbitrary x in scope, have:
(4) utilize Lagrange algorithm interpolation calculation photography time scanTime camera relative to the threeaxis attitude angle (Roll, Pitch, Yaw) of track system of coordinates;
(5) according to camera laboratory measurements, the sample the CCD reading image coordinate (sample, the line) correspondence of reference mark dominating pair of vertices visits optical axis sensing angle (psiX, psiY) of unit.
(6) rigorous geometry model at reference mark place is set up, for arbitrary reference mark, the strict imaging geometry model that the strict geometry imaging model Model linear array push of data construct remote sensing image sweeps camera when utilizing its interior orientation, track, attitude, row etc. as follows described in.
Wherein:
[X_{S},Y_{S},Z_{S}] for inscribing the position of satellite in agreement geocentric coordinates system time this, i.e. track position, reference mark place (PX, PY, PZ);
[X_{G},Y_{G},Z_{G}] it is the coordinate of terrain object point corresponding to this picture unit in agreement geocentric coordinates system.
PsiX, psiY be respectively image corresponding camera picture unit primary optical axis unit vector and satellite body system of coordinates Xaxis, Yaxis angle
U scale factor.
M_{0}For satellite body system of coordinates is relative to the installation matrix of camera, obtain by ground survey before satellite is launched;
M_{1}For inscribing satellite time this to track system of coordinates rotation matrix, it is made up of the attitude angle measured on star.
M_{2}For this moment lower railway is to J2000.0 system of coordinates rotation matrix, it is made up of dragon's head right ascension, orbital inclination, argument etc.
M_{3}For inscribing J2000.0 to WGS84 system of coordinates rotation matrix time this, precession of the equinoxes correction, nutating correction, Greenwich sidereal time correction and pole need to be carried out and move correction.
3. calculating positioning error size and the direction at each reference mark place, preliminary excluding gross error reference mark, obtains initial control point pair
(1) according to the rigorous geometry model set up, based on the auxiliary data such as when interior orientation, place, each reference mark element, track, attitude, row, calculate the initial ground point location coordinate (lon ', lat ') of original picture point (sample, line) in each reference mark
(2) image spot location (lon, lat, height) coordinate information is controlled according to each reference mark high precision, the initial fix error (�� xi, �� yi) of scaling system picture point. For each reference mark,
(2.1) by initial ground point location coordinate (lon ', lat ') after utm projection, its planimetric coordinates (x ', y ') is obtained
(2.2) by reference mark imaging point coordinate (lon, lat, height) after utm projection, its planimetric coordinates (x, y) is obtained
(2.3) definition initial fix error (�� xi, �� yi)=(xx ', yy ')
(3) calculation control point is to positioning error mean value (�� x, �� y)
(4) to each dominating pair of vertices, judge that its initial fix error { whether in [�� x �� 20% �� x, �� y �� 20% �� y] scope, if not within the scope of this, rejected by �� xi, �� yi} by this reference mark. After rejecting error control point, obtain initial control point pair, for setting up outer orientation element geometry calibration mathematical model
4. set up camera outer orientation element geometry calibration mathematical model, calculate each reference mark process opinion pointing vector and actual pointing vector.
(1) set up camera outer orientation element geometry calibration mathematical model, in strict imaging geometry model, introduce error matrix M, as follows
(2) to arbitrary group of reference mark, the actual pointing vector V at original image picture point (sample, line) place is calculated_{Actual}:
Element (psiX, psiY) calculation control point place, the interior orientation optical axis at reference mark place is utilized to point to vector V in camera coordinates system, as the actual pointing vector V at this reference mark place after being normalized_{Actual};
(3) to arbitrary group of reference mark, the theoretical pointing vector V at calculation control point place_{Theoretical}:
(3.1) utilizing reference mark ground point location coordinate (lat, lon, height) to calculate the three axle positions (XG, YG, ZG) of ground millet cake in agreement geocentric coordinates system, calculation formula is as follows:
(3.2) the threeaxis attitude angle (Roll, Pitch, Yaw) at reference mark place is utilized to calculate by camera coordinates system to the rotation matrix M1 of track system of coordinates
(3.3) utilize the satellite orbital position (PX, PY, PZ, VX, VY, VZ) at reference mark place to calculate the track six roots of sensation number of satellite under J2000.0 system of coordinates, thus calculate the rotation matrix M2 of track system of coordinates to J2000.0 system of coordinates
(3.4) utilize the imaging time at reference mark place, calculate the rotation matrix M3 of J2000.0 system of coordinates to WGS84 system of coordinates
(3.5) the theoretical pointing vector V at calculation control point place_{Theoretical}, calculation formula is:
5. set up three axle rotation error equations between theoretical pointing vector and actual pointing vector, equation is carried out linearization process
(1) set error matrix M as three axle rotating orthogonal matrixes, set up the matrix conversion model between theoretical pointing vector and actual pointing vector; Medial error matrix M of the present invention is characterized by around X, Y, the orthogonal matrix that the angle that Z coordinate axle rotates is formed, as follows:
Wherein:
(2) model is changed, obtain the nonlinear equation of the theoretical each parameter of pointing vector with each parameter of actual pointing vector, as follows:
(3) nonlinear equation is carried out Taylor expansion at initial point (0,0,0) place, obtain the linearapporximation equation of nonlinear equation.
Wherein:
(4) for each group of theoretical pointing vector and actual pointing vector, calculate linear equation parameter, obtain system of linear equations
Utilize the system of linear equations between three axle rotation error establishing equations its theoretical pointing vector and actual pointing vector, for N number of reference mark, system of linear equations can be obtained as follows:
6. method of least squares iteration resolves error equation parameter, obtains demarcating outer parameter
(6.1) the error equation M1 set up for N number of reference mark, leastsquares iteration resolves equation parameter
For the large scale sparse linear equations Ax=b that error equation calculates out, adopt iterative method solving equation numerical solution. For equation Ax=b, set up its recursion formula:
x_{i+1}=(A^{T}A+E)^{1}A^{T}b+(A^{t}A+E)^{1}x_{i}
Set up least square solution error equation:
��=x_{i+1}x_{i}=(A^{T}A+E)^{1}A^{T}b+(A^{t}A+E)^{1}x_{i}x_{i}
The orthogonal each parameter of error matrix M is all similar to and equals 0, therefore for x composes initial value (0,0,0).
Sequence vector x is resolved according to recursion formula_{0},x_{1},x_{2},...,x_{n}, utilize minimum meansquare error equation solver ��_{1},��_{2},...,��_{n}, until ��_{k}�� min, nowIt is the least square solution of this equation.
(6.2) using the least square solution of equation M1 as preliminary outer orientation parameter calibration result, substitute into the strict imaging geometry model at each reference mark place, calculate the calibration residual error at each reference mark place, the reference mark that deleted residual is big.
(6.3) utilizing M reference mark after rejecting excessive residual error, set up error equation M2, leastsquares iteration resolves equation parameter. The least square solution calculated is as outer orientation parameter calibration result. Calibration result is applied in Image correction in remote sensing algorithm, strict imaging geometry model parameter is revised, it is possible to significantly improve the nothing control geometric positioning accuracy of this camera remote sensing image product.
As shown in table 1table 3, the outer orientation parameter calibration result that table 1 calculates for the present invention, table 2 is not for using VRSS1 satellite geometry positioning precision measuring result before the present invention, and table 3 carries out the geometric positioning accuracy measuring result of VRSS1 satellite after calibration inorbit for utilizing the present invention. VRSS1 satellite outer orientation parameter is being rolled and all there occurs bigger change in pitching two as can be seen from Table 1, reach 0.06 measurement level, average orbit altitude (official's nominal value is 640 kilometers) measuring and calculating according to VRSS1 satellite, the outer parameter that rotating direction0.06239 is spent can cause ground to be about 640000 �� tan (0.06239)=696.1 meter without control positioning precision; Pitch orientation 0.06804 is outside one's consideration, and without controlling, positioning precision is about 640000 �� tan (0.06804)=760.07 meter on parameter inducible ground; The inducible overall error of rolling pitch orientation isBefore adopting the present invention, the actual measurement of satellite is 1073.18 meters of (mean value) magnitudes without control positioning precision as can be seen from Table 2, it is seen that what outer orientation parameter caused is consistent with measured value substantially without control positioning error. As can be seen from Table 3, after adopting the present invention that satellite is carried out outer orientation parameter calibration, the actual measurement of satellite is 36.31 meters of (mean value) magnitudes without control positioning precision, and the nothing control positioning precision of this satellite is improve 29 times by the present invention.
Table 1
Table 2
Table 3
The part that the present invention does not elaborate belongs to techniques well known.
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