CN110956588B - Image high-precision geometric correction method based on shortest distance of encrypted points - Google Patents
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Abstract
The invention provides an image high-precision geometric correction method based on the shortest distance of an encryption point, which comprises the steps of firstly determining object coordinates according to a coordinate forward calculation function, then determining the object coordinates of each pixel on a corrected image according to the object coordinate range and a resampling interval, extracting the maximum value of the number of pixels on the corrected image corresponding to 1 pixel on an original image, and recording the maximum value as N; setting the geometric correction control precision as e pixels, encrypting each pixel of an original image into a point of [ N/e ] x [ N/e ], calculating object coordinates of an encrypted point by using a coordinate forward calculation function, calculating image coordinates of the encrypted point on the corrected image one by one, and calculating the nearest integer pixel position; for each whole pixel position on the corrected image, if a plurality of corresponding encrypted point coordinates are found, determining a corresponding relation by comparing distance errors; and (4) interpolating gray scale from the original image according to the corresponding encrypted point coordinates of the position of each point of the corrected image to obtain a complete output image.
Description
Technical Field
The invention relates to the technical field of geometric correction of images, in particular to geometric correction by a direct method, and relates to a high-precision geometric correction method of images based on the shortest distance of encrypted points.
Background
The optical sensor adopting the linear array swing scanning imaging can be suitable for the condition with an infrared wave band, the imaging field angle is large, the image coverage range is wide, for example, a marine satellite water color scanner can cover the world once a day, and the optical sensor has great advantages in the aspect of dynamic monitoring. However, its geometric imaging model is more complex than linear array push-broom imaging, mainly due to: when the linear array is pushed to scan, pixels of each row are imaged at the same time, so that the linear array has the same parameters of track, attitude and the like; when the linear array is swept, each pixel of each row is imaged at different time, and has different track attitude parameters. This results in a different way of subsequent geometry processing: when the linear array is pushed and swept, the high-efficiency indirect geometric correction can be realized by establishing a rational polynomial model equivalent to a collinear equation model; when the linear array is swept, geometric correction can be carried out only by a general direct method or a mode of combining the direct method and a local approximate indirect method. The correction is carried out by adopting a general direct method or a mode of combining the direct method and a local approximate indirect method, and the problem of insufficient precision exists.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a high-precision geometric correction method for an image based on the shortest distance between encrypted points.
The technical scheme of the invention provides an image high-precision geometric correction method based on the shortest distance of an encryption point, which comprises the following steps:
1) firstly, determining an object coordinate according to a coordinate forward calculation function, then determining the object coordinate of each pixel on the corrected image according to the object coordinate range and the resampling interval, extracting the maximum value of 1 pixel on the original image corresponding to the number of pixels on the corrected image and recording the maximum value as N;
2) setting the geometric correction control precision as e pixels, encrypting each pixel of an original image into a point of [ N/e ] × [ N/e ], and solving object-side coordinates (lon, lat) of the encrypted point (sx, sy) by using a coordinate normal calculation function, wherein [ ] represents upward integer;
calculating image coordinates (X, Y) of the encrypted points on the corrected image one by one, and solving the nearest whole pixel position (X0, Y0);
for each integer pixel position (X0, Y0) on the corrected image, if a plurality of corresponding encrypted point coordinates are found, the corresponding relation is determined by comparing the distance errors;
3) and (4) interpolating gray scale from the original image according to the corresponding encrypted point coordinates of the position (X0, Y0) of each point of the corrected image to obtain a complete output image.
Furthermore, the coordinate forward function is as follows,
knowing that the image space coordinate of a certain point P is (s, l), according to the camera pointing angle model, obtaining a three-dimensional vector v of the point P in a camera coordinate system cam ＝[x C (s) y(s) 1]', wherein a 0 ，a 1 ，a 2 ，b 0 ，b 1 ，b 2 Are the camera pointing angle model coefficients,
according to the line array sweep imaging principle, the imaging time T of the point P is obtained as Tl 0 + Δ t × s, where Tl 0 The imaging time of the 0 th pixel of the l-th row and delta T are the exposure time interval of each pixel, and the position [ X ] of the satellite is obtained according to T interpolation T Y T Z T ]' speed and attitude, and obtaining a transformation matrix R from the J2000 coordinate system to the WGS84 and from the body coordinate system to the J2000 coordinate system at the time T T ，R GFFB ；
Let R BS Is a transformation matrix from the camera coordinate system to the body coordinate system, and then from the image side coordinates (s, l) to the object side coordinate vector v obj ＝[X Y Z]The coordinate forward function of' is as follows,
the object space coordinates (X, Y, Z) also satisfy the WGS84 ellipsoid equation,
where A, B are the major and minor half axes (known constants) of the WGS84 ellipsoid, and H is the elevation of the object-side coordinates (X, Y, Z) on the WGS84 ellipsoid.
Furthermore, the implementation of calculating N is as follows,
for two adjacent integers on the original imageCounting points, wherein the corresponding object space coordinates are (Xs, Ys), (Xt, Yt), respectively, and the pixel distance of the two points on the corrected image is GSD by setting the resampling interval of the corrected image as
The maximum value of all d is found and rounded up to yield N.
Then, based on the object coordinates (lon, lat) of the encrypted point (sx, sy), the video coordinates (TX, TY) of the encrypted point on the corrected video are calculated as follows,
TX＝(lon-Xmin)/GSD，TY＝(Ymax-lat)/GSD
wherein, (Xmin, Ymax) is the object coordinates of the top left corner of the outputted corrected image.
And, performing steps 1) to 3) in parallel by adopting an image blocking mode.
Compared with the prior art, the method provided by the invention has controllable geometric correction precision, supports the realization of rapid processing in a parallel calculation mode, and can be widely applied to the problem of coordinate back calculation of any known coordinate forward calculation function, such as aviation and aerospace linear array push-broom mode imaging, aviation linear array swing-broom mode imaging and area array imaging.
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FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Detailed Description
For better understanding of the technical solutions of the present invention, the present invention will be further described in detail with reference to the accompanying drawings and examples.
Referring to fig. 1, an embodiment of the present invention provides a method for high-precision geometric correction of an image based on a shortest distance between encryption points, including the following steps:
1) firstly, determining an object coordinate range of an original image according to a coordinate forward calculation function, then determining an object coordinate of each pixel on the corrected image according to the object coordinate range and a resampling interval (which is a preset value and is usually a ground resolution corresponding to a central point of the original image), and calculating the maximum value of the number of pixels on the corrected image corresponding to 1 pixel on the original image and recording the maximum value as N (rounding up).
The coordinate forward function is as follows:
knowing that the image space coordinate of a certain point P is (s, l), according to the camera pointing angle model, obtaining a three-dimensional vector v of the point P in a camera coordinate system cam ＝[x C (s) y(s) 1]', wherein a 0 ，a 1 ，a 2 ，b 0 ，b 1 ，b 2 Are camera pointing angle model coefficients (known).
According to the line array sweep imaging principle, the imaging time T of the point P is obtained as Tl 0 + Δ t × s, where Tl 0 At the imaging time of the 0 th pixel in the ith row, Δ t is the exposure time interval of each column of pixels. Interpolating from T to obtain the position of the satellite [ X ] T Y T Z r ]' speed, attitude, etc., to obtain a transformation matrix R from the J2000 coordinate system to the WGS84 and from the body coordinate system to the J2000 coordinate system at time T T ，R GFFB 。
Then set R BS Is a transformation matrix (known value) from the camera coordinate system to the body coordinate system, then from the image side coordinates (s, l) to the object side coordinate vector v obj ＝[X Y Z]' the coordinate forward function is as follows:
the objective coordinates (X, Y, Z) also satisfy the WGS84 ellipsoid equation:
where A, B are the major and minor semi-axes (known constants) of the WGS84 ellipsoid, and H is the elevation of the object-side coordinates (X, Y, Z) on the WGS84 ellipsoid.
The process of solving the object-side coordinates (X, Y, Z) from the image-side coordinates (s, l) according to the above coordinate forward function is as follows:
a) let H be 0
The three unknowns (XYZ) in equation 2 are expressed as a linear expression for the parameter m to be solved:
substituting (formula 4) into (formula 3), solving a quadratic equation of one unit about m, and (truncating the larger value) to obtain m. Substituting m into (formula 2) to solve the object coordinates (X, Y, Z).
b) And then interpolating from the object space coordinates (X, Y, Z) according to the known DEM to obtain a more accurate elevation H, repeating the above process 1, and resolving the more accurate object space coordinates (X, Y, Z).
c) And stopping iteration when the difference between two adjacent calculation results of the object coordinates (X, Y, Z) is smaller than a preset threshold value. In specific implementation, the threshold value can be preset according to the precision requirement.
The object coordinate range of the original image is calculated as follows:
the size of the original image is known, the width w and the height h of the original image are set, four corner point coordinates (0, 0), (w-1, 0), (0, h-1) and (w-1, h-1) are respectively substituted into a coordinate positive transformation function, object coordinates (X1, Y1), (X2, Y2), (X3, Y3), (X4 and Y4) of the corresponding four corner points are obtained, and the maximum and minimum coordinate ranges (Xmin-Xmax and Ymin-Ymax) of the four corner points are obtained, namely the object coordinate range of the original image.
The implementation of calculating N is as follows:
for two adjacent integer points on the original image, the pixel coordinates are (sx, sy), (sx +1, sy) or (sx, sy), (sx, sy +1), (wherein sx is more than or equal to 0 and less than w-1, sy is more than or equal to 0 and less than h-1), the corresponding object coordinates are (Xs, Ys), (Xt, Yt), the corrected shadow is setThe resampling interval of the image is GSD, and the pixel distance of the two points on the corrected image is
And respectively calculating the pixel distance d of all adjacent integer points on the original image, then calculating the maximum value of all d, and rounding up to obtain N.
2) If the preset geometric correction control precision is set as e pixels (where 0 < e < 1, and e is 0.2), each pixel of the original image is encrypted to [ N/e ]]×[N/e]Point of (wherein [ ]]Expressing rounding up), and calculating object coordinates (lon, lat) of each encrypted point (sx, sy) on the original image by using a coordinate forward calculation function; the image coordinates (TX, TY) of each encrypted point on the corrected image are calculated, the nearest integer pixel position (TX0, TY0) is obtained, and the distance error is calculated
Based on the principle of shortest distance, determining the original image coordinates of the encrypted points corresponding to all integer pixel positions (TX0, TY0), namely for a certain integer pixel position (X0, Y0) on the corrected image, if a plurality of corresponding encrypted point coordinates are found, determining the corresponding relation by comparing distance errors, and finally obtaining an original coordinate lookup table and a corresponding error table.
The embodiment encryption point operation is implemented as follows:
the original image is interpolated, assuming that there are four whole pixels (sx, sy), (sx +1, sy), (sx, sy +1), (sx +1, sy +1) on the original image, and after 5 × 5 encryption, there are 25 encrypted points in total, and the image space coordinates of the encrypted points are:
(sx，sy)，(sx+0.25，sy)，(sx+0.5，sy)，(sx+0.75，sy)，(sx+1，sy)， (sx，sy+0.25)，(sx+0.25，sy+0.25)，(sx+0.5，sy+0.25)，(sx+0.75，sy+0.25)，(sx+1，sy +0.25)， (sx，sy+0.5)，(sx+0.25，sy+0.5)，(sx+0.5，sy+0.5)，(sx+0.75，sy+0.5)，(sx+1，sy+0.5) (sx，sy+0.75)，(sx+0.25，sy+0.75)，(sx+0.5，sy+0.75)，(sx+0.75，sy+0.75)，(sx+1，sy +0.75)， (sx，sy+1)，(sx+0.25，sy+1)，(sx+0.5，sy+1)，(sx+0.75，sy+1)，(sx+1，sy+1)
and (4) calculating the two-dimensional object space coordinates (lon, lat) corresponding to the encrypted points by adopting the previous coordinate positive transformation function.
The image coordinates (TX, TY) of the encrypted points on the corrected image are calculated as follows:
TX＝(lon-Xmin)/GSD，TY＝(Ymax-lat)/GSD
wherein the content of the first and second substances,
(Xmin, Ymax) is the object coordinates of the top left corner of the output corrected image, i.e. the minimum of lon and the maximum of lat;
(lon, lat) is the object coordinates of the encrypted point on the output corrected image, generally the horizontal axis is positive to east and the vertical axis is positive to north;
(TX, TY) is the image coordinates of the encrypted points on the output corrected image, and generally the horizontal axis is positive to the right, the vertical axis is positive downward, and the top left corner point is the origin of the image coordinate system.
Finding the nearest whole pixel position of the encrypted point on the corrected image (TX0, TY 0):
TX0 ═ TX, TY0 ═ TY, where [ ] denotes the nearest integer
Looping in the above manner, for each encrypted point (sx, sy) on the original image, its nearest integer pixel position (TX0, TY0) and distance error err on the corrected image are obtained.
Conversely, at the integer pixel position (TX0, TY0) on the corrected image, the original image coordinates (sx, sy) of the encrypted points are recorded one by one, while the distance error err is recorded.
Since the distance between the encrypted points is small, the nearest whole pixel position on the corrected image calculated by the adjacent encrypted points may be the same coordinate value (TX0, TY0), and at this time, the distance error err needs to be compared:
in one embodiment, the corrected image may be pre-allocated with a space Q, and a specific value, such as-9999, -999999, etc., may be initialized to indicate that the space has not been assigned. Each encrypted point calculates in parallel the corresponding integer pixel position (TX0, TY0) and distance error err on the corrected image and assigns (sx, sy) to the integer pixel position (TX0, TY0) of the pre-allocated space Q. During value assignment, if the corresponding encrypted point image coordinates (sx, sy) obtained before are found at the integer pixel positions (TX0, TY0), comparing the distance error err, and replacing the encrypted point image coordinates with larger distance errors by the encrypted point image coordinates with smaller distance errors based on the shortest distance principle; when the distance errors are completely the same, any one of the distance errors is selected, and the average value of the coordinates of the two encrypted points can be recorded in specific implementation. And finally obtaining an original coordinate lookup table and an error table.
3) And (3) interpolating gray scale from the original image according to the corresponding encrypted point image coordinates (sx, sy) determined by (2) according to the position (TX0, TY0) of each point of the corrected image to obtain a complete output image, namely the corrected image, wherein the geometric positioning precision of the image is reflected from an error table.
In practice, common gray-level resampling methods, such as nearest neighbor method, bilinear interpolation method, and bicubic convolution method, can be used.
In specific implementation, the automatic operation of the process can be realized by adopting a software mode. The image space to the object space can be realized by partitioning the original image and processing each image in parallel. For example, after the image is divided into blocks, steps 1) to 3) are executed in parallel for each block. The apparatus for operating the process should also be within the scope of the present invention.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives in a similar manner to those skilled in the art to which the present invention pertains.
Claims (4)
1. A high-precision geometric correction method for images based on the shortest distance between encrypted points is characterized by comprising the following steps:
1) firstly, determining an object coordinate according to a coordinate forward calculation function, then determining the object coordinate of each pixel on the corrected image according to the object coordinate range and the resampling interval, extracting the maximum value of the number of pixels on the corrected image corresponding to 1 pixel on the original image, and recording the maximum value as N; the implementation of the calculation of N is as follows,
for two adjacent integer points on the original image, the corresponding object coordinates are (Xs, Ys), (Xt, Yt), respectively, and the pixel distance of the two points on the corrected image is GSD by setting the resampling interval of the corrected image as
Solving the maximum value of all d, and rounding up to obtain N;
2) setting the geometric correction control precision as e pixels, encrypting each pixel of an original image into a point of [ N/e ] x [ N/e ], and calculating object-side coordinates (lon, lat) of an encrypted point (sx, sy) by using a coordinate positive calculation function, wherein [ ] represents an upward integer;
calculating image coordinates (X, Y) of the encrypted points on the corrected image one by one, and solving the nearest whole pixel position (X0, Y0);
for each integer pixel position (X0, Y0) on the corrected image, if a plurality of corresponding encrypted point coordinates are found, the corresponding relation is determined by comparing the distance errors;
3) and (4) interpolating gray scale from the original image according to the corresponding encrypted point coordinates of the position (X0, Y0) of each point of the corrected image to obtain a complete output image.
2. The method for correcting the geometry of an image with high precision based on the shortest distance between encrypted points as claimed in claim 1, wherein: the coordinate forward function is as follows,
knowing that the image side coordinate of a certain point P is (s, l), calculating a three-dimensional vector v of the point P in a camera coordinate system according to a camera pointing angle model cam ＝[x C (s) y C (s) 1]', wherein a 0 ,a 1 ,a 2 ,b 0 ,b 1 ,b 2 Are the camera pointing angle model coefficients,
imaging according to linear array sweepIn principle, the imaging time T ═ Tl at the point P is obtained 0 + Δ t × s, where Tl 0 The imaging time of the 0 th pixel of the l-th row and delta T are the exposure time interval of each pixel, and the position [ X ] of the satellite is obtained according to T interpolation T Y T Z T ]' speed and attitude, and obtaining a transformation matrix R from the J2000 coordinate system to the WGS84 and from the body coordinate system to the J2000 coordinate system at the time T T ,R GFFB ；
Let R BS Is a transformation matrix from the camera coordinate system to the body coordinate system, and then from the image side coordinates (s, l) to the object side coordinate vector v obj ＝[X Y Z]The coordinate forward function of' is as follows,
the object space coordinates (X, Y, Z) also satisfy the WGS84 ellipsoid equation,
wherein, A and B are the major half axis and the minor half axis of a WGS84 ellipsoid, and H is the elevation of an object coordinate (X, Y and Z) on the WGS84 ellipsoid.
3. The method for correcting the geometry of an image with high precision based on the shortest distance between encrypted points as claimed in claim 2, wherein: based on the object coordinates (lon, lat) of the encrypted point (sx, sy), the image coordinates (TX, TY) of the encrypted point on the corrected image are calculated as follows,
TX＝(lon-Xmin)/GSD,TY＝(Ymax-lat)/GSD
wherein, (Xmin, Ymax) is the object coordinates of the top left corner of the outputted corrected image.
4. The method for correcting the geometry of an image with high precision based on the shortest distance between encrypted points according to claim 1, 2 or 3, wherein: and (4) performing the steps 1) to 3) in parallel by adopting an image partitioning mode.
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