CN111862227B - On-orbit non-uniformity correction method of mechanical staggered spliced camera based on complex scene - Google Patents
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Abstract
The invention relates to an on-orbit relative calibration method of a mechanical staggered spliced remote sensing camera based on a complex scene. The technology comprises the steps of carrying out orthogonal secondary imaging on a complex scene before and after rotation of the camera and carrying out non-uniformity correction through gray value multipoint curve fitting by utilizing the characteristic that a satellite can rotate around a yaw axis of the camera so as to achieve the purpose of carrying out non-uniformity correction on the mechanical staggered spliced CCD camera. Experiments show that the technology can effectively reduce the non-uniformity of the CCD camera, improve the quality of remote sensing images and provide effective data for subsequent image processing.
Description
Technical Field
The invention relates to an image processing technology, in particular to an on-orbit relative calibration method of a mechanical staggered spliced remote sensing camera based on a complex scene.
Background
With the development of space remote sensing technology, high space-time resolution and wide field of view gradually become research emphasis of push-broom space remote sensing cameras. In order to make the push-broom type space camera have a wider field of view, the size of a single CCD device cannot meet the current requirements, and a plurality of CCD devices need to be spliced in a staggered manner to realize large-field imaging, so that an on-orbit satellite has a larger imaging breadth.
For a push-broom imaging optical remote sensing satellite with strong agility, when in-orbit relative radiation calibration is carried out, a method of rotating a camera around a satellite yaw axis by a certain angle (usually about 90 degrees) can be used for acquiring a relatively uniform image to correct non-uniformity among different probe elements. This is known as the Side-slip scaling method, also known as yaw scaling.
The mechanical staggered spliced CCD camera focal plane is formed by assembling a plurality of CCDs into a two-line staggered focal plane mode, namely, the CCDs in the second line just fill gaps formed by the CCDs in the first line, and pixels at the head and the tail are respectively aligned, but the two lines are staggered at a certain position in the flight direction (image integration direction) of the camera. When the camera uses the traditional yaw calibration method to carry out non-uniformity correction, the non-uniformity between two rows of CCDs is difficult to reduce, and the phenomenon of stripe non-uniformity exists in the corrected image.
Disclosure of Invention
The invention provides an on-orbit relative calibration method of a mechanical staggered spliced remote sensing camera based on a complex scene, which aims to solve the problem of stripe non-uniformity when a traditional yaw calibration method is used for carrying out non-uniformity correction on the mechanical staggered spliced CCD camera.
The on-orbit relative calibration method of the mechanical staggered spliced remote sensing camera based on the complex scene is based on a yaw calibration method and the mechanical staggered spliced characteristic of a CCD camera, and the non-uniformity of the image is reduced through twice correction before and after rotation of the camera; the specific process is as follows:
step 1: and inputting the image data of the yaw imaging mode to obtain a gray matrix of the yaw imaging data. Because of the characteristic of staggered spliced focal planes, imaging between yaw imaging sheets is discontinuous, an imaging area of a sensor needs to be intercepted, the degree of a connecting line included angle in an actual yaw radiometric calibration image is calculated by using a LSD (Line Segment Detector) linear detection algorithm, and according to the detection result of the yaw radiometric calibration data included angle, the yaw radiometric calibration data is subjected to prescribing treatment, so that each line of data in the image is imaging data of all detection elements of the sensor on the same ground object;
step 2: using the data of the step 1, performing multipoint curve fitting on the imaging elements of each probe element by a least square method, solving the non-uniformity correction coefficient of each probe element in each array of the focal plane, and correcting the conventional imaging mode image by using the coefficient;
step 3: and (3) using the corrected image obtained in the step (2), performing multipoint curve fitting on the spliced pixels of the adjacent arrays by a least square method, solving the non-uniformity correction coefficient between focal plane arrays, and correcting the image by using the coefficient.
Further, the step 2 specifically includes:
firstly, according to the method of the step 1, the yaw radiometric calibration data is subjected to prescribing treatment, so that pixels in each row are imaged on the same ground object; then, the gray average value of each row of pixels is calculated to obtain a column of pixel average value data; then, each column of data is taken, and a least square method is utilized to calculate a conversion coefficient between the data and pixel average value data, wherein the conversion coefficient is a non-uniformity correction coefficient; and finally, correcting the conventional imaging mode image by using the correction coefficient.
Further, the step 3 specifically includes:
firstly, taking an overlapped part image group from a conventional imaging mode image after first correction; then, after the data of each image group are horizontally aligned, N columns of data groups (N can be set according to the pixel width of the overlapping area) in the middle of each image group are taken; then, a least square method is used for solving conversion coefficients among the data sets, wherein the conversion coefficients are non-uniformity correction coefficients among CCD arrays; and finally, performing second correction on the conventional imaging mode image by using the correction coefficient.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
the method is applicable to the non-uniformity correction of the mechanical staggered spliced CCD camera, solves the problem of stripe non-uniformity in the non-uniformity correction of the mechanical staggered spliced CCD camera by the traditional yaw calibration method, effectively reduces the image non-uniformity, and has good applicability.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a schematic view of focal plane of mechanically interlaced CCD camera
FIG. 3 is an uncorrected conventional imaging mode image;
FIG. 4 is a yaw imaging mode image;
FIG. 5 is a plot of the degrees of line angle in a yaw radiometric calibration image;
FIG. 6 is a yaw radiometric image specification;
FIG. 7 is a first corrected image;
fig. 8 is a graph showing the comparison of images before and after correction.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
according to the mechanical staggered spliced remote sensing camera on-orbit relative calibration method based on the complex scene, the non-uniformity correction algorithm is based on a yaw calibration method and the mechanical staggered spliced characteristic of a TDICCD camera, and the non-uniformity of an image is reduced through twice correction before and after rotation of the camera, and the flow is shown in a figure 1.
The non-uniformity correction method for the mechanical staggered spliced TDICCD camera comprises the following specific steps of:
step 1: and inputting the image data of the yaw imaging mode to obtain a gray matrix of the yaw imaging data. Because of the characteristic of staggered spliced focal planes, imaging between yaw imaging sheets is discontinuous, an imaging area of a sensor needs to be intercepted, the degree of a connecting line included angle in an actual yaw radiometric calibration image is calculated by using a LSD (Line Segment Detector) linear detection algorithm, and according to the detection result of the yaw radiometric calibration data included angle, the yaw radiometric calibration data is subjected to prescribing treatment, so that each line of data in the image is imaging data of all detection elements of the sensor on the same ground object;
step 2: firstly, translating the image data of the yaw imaging mode which is determined to be available in the step 1, dividing the image data into two image blocks according to odd-even serial numbers as shown in fig. 2, and enabling pixels in each row of each block to be imaged on the same ground object; then, the gray average value of each row of pixels is calculated to obtain average value data of two columns of pixels; then, each column of data is taken, multipoint curve fitting is carried out on the data and the average value data of the corresponding pixels, and a conversion coefficient between the data and the average value data is calculated by using a least square method, wherein the coefficient is a non-uniformity correction coefficient; and finally, correcting the conventional imaging mode image by using the correction coefficient.
Step 3: firstly, taking a spliced part image group (image data formed by overlapping areas in fig. 2) from a conventional imaging mode image after first correction; then after the data of each image group are horizontally aligned, N columns of data groups (N can be set according to the pixel width of the overlapping area) in the middle of each image group pair are taken; then, carrying out multipoint curve fitting on each data set by utilizing a least square method, and solving a conversion coefficient among the data sets, wherein the conversion coefficient is a non-uniformity correction coefficient among the TDICCD arrays; and finally, performing second correction on the conventional imaging mode image by using the correction coefficient.
Specific examples of the non-uniformity correction are as follows:
fig. 3 is an uncorrected conventional imaging mode image, where narrow stripe non-uniformities (inter-pixel non-uniformities) and wider stripe non-uniformities (inter-array non-uniformities) are apparent. Fig. 4 is a yaw-imaging mode image, and it is obvious that the 1 st, 3 rd and 5 th blocks are identical, and the 2 nd and 4 th blocks are identical, because a certain distance exists between the two rows of CCD arrays, and the features are different in yaw-mode imaging.
The application step I: and inputting the image data of the yaw imaging mode to obtain a gray matrix of the yaw imaging data. Because of the characteristics of staggered spliced focal planes, imaging between yaw imaging sheets is discontinuous, an imaging area of a sensor needs to be intercepted, the degree of a connecting line included angle in an actual yaw radiometric calibration image is calculated by using a LSD (Line Segment Detector) straight line detection algorithm, and according to the detection result of the yaw radiometric calibration data included angle, the yaw radiometric calibration data is subjected to prescribing treatment, so that each line of data in the image is imaging data of all detection elements of the sensor on the same ground object. The detection result of the straight line included angle is shown in fig. 5, and the yaw calibration data is defined in fig. 6.
The formulation formula is as follows:
wherein; DN (i, j) represents the image gray value at the ith row j column of yaw data specification; DN (digital subscriber line) SS Defining pre-conversion image gray values for the yaw radiometric calibration image; θ is the actual angle of the yaw image.
The second application step: using the data specified in the step 1, solving the conversion relation between the column pixel gray value formed by each probe element and the column pixel gray average value through a least square method, namely solving the non-uniformity correction coefficient of each probe element in each array of the focal plane, and correcting the conventional imaging mode image by using the coefficient so as to achieve the non-uniformity correction among all probe elements in the single-chip detection array, wherein the correction result is shown in figure 7;
solving the correction coefficient by using a least square method to make the gray value of a row of pixels formed by a certain probe element to be corrected be { x } i I=1, 2,..r }, the gray average value column of each column of pixels formed by each probe element is { y } i |i＝1,2,...,r}，x i ,y i Within a plane, a set of discrete points { (x) i ,y i ) I=1, 2,..r }, a multi-point curve fitting is performed on the discrete point set by using the following formula, and each coefficient of the fitted curve is a correction coefficient.
Linear form fit of linear y=a 0 +a 1 The formula for solving the coefficient is x:
quadratic function fitted curve y=a 0 +a 1 x+a 2 x 2 The equation for solving the coefficients is:
wherein r is the number of discrete points; { a 0 ,a 1 ,...,a n And the coefficients of each item of the fitting curve are shown.
And (3) an application step: the corrected image obtained in the step 2 is a mechanical staggered spliced camera, and the conversion coefficient of each adjacent array is solved by using the least square method in the step 2 between the spliced areas, namely the non-uniformity correction coefficient between focal plane arrays is solved, and the image is corrected by using the coefficient, so that the correction result between detection arrays is as shown in fig. 8, and the non-uniformity of the image is reduced from 6.236% to 0.660%.
While the foregoing is directed to embodiments of the present invention, other and further details of the invention may be had by the present invention, it should be understood that the foregoing description is merely illustrative of the present invention and that no limitations are intended to the scope of the invention, except insofar as modifications, equivalents, improvements or modifications are within the spirit and principles of the invention.
Claims (2)
1. The on-orbit relative calibration method of the mechanical staggered spliced remote sensing camera based on the complex scene is characterized in that the method is based on the mechanical staggered spliced characteristic of the CCD camera, the complex scene is subjected to orthogonal secondary imaging before and after rotation of the camera, and the non-uniformity of the image is reduced through secondary correction; the correction method comprises the following specific processes:
step 1: inputting image data of a yaw imaging mode, obtaining a gray matrix of the yaw imaging data, intercepting a sensor imaging area, calculating the degree of a connecting line included angle in an actual yaw radiometric calibration image by using an LSD straight line detection algorithm, and prescribing the yaw radiometric calibration data according to the detection result of the yaw radiometric calibration data included angle to ensure that each line of data in the image is imaging data of all detection elements of the sensor on the same ground object; in the step 1, the specifying of the yaw radiometric calibration data is specifically:
where DN (i, j) represents the image gray value at the ith row and j column of yaw data specification; DN (digital subscriber line) SS Defining pre-conversion image gray values for the yaw radiometric calibration image; θ is the actual angle of the yaw image;
step 2: using the data in the step 1, performing multipoint curve fitting on imaging elements of each probe element by a least square method, solving a non-uniformity correction coefficient of each probe element in each array of a focal plane, and correcting a conventional imaging mode image by using the non-uniformity correction coefficient; the step 2 specifically comprises the following steps:
step 2.1, using the yaw imaging data specified in the step 1 to calculate the gray average value of each row of pixels, and obtaining a column of pixel average value data;
step 2.2, taking each column of data, and calculating a conversion coefficient between the data and pixel average value data by using a least square method, wherein the coefficient is a non-uniformity correction coefficient; finally, correcting the conventional imaging mode image by using the correction coefficient;
step 3: using the corrected image obtained in the step 2, performing multipoint curve fitting on the spliced pixels of the adjacent arrays by a least square method, solving a non-uniformity correction coefficient between focal plane arrays, and correcting the image by using the coefficient;
the step 3 specifically comprises the following steps:
step 3.1, taking overlapping part image groups from the conventional imaging mode images after the first correction;
step 3.2, the imaging of the front array and the rear array needs to be translated to a certain extent during splicing, and the translation distance is determined by the design of a specific camera focal plane; after the data of each image group are horizontally aligned, taking out N columns of data groups in the middle of each image group;
and 3.3, obtaining conversion coefficients among the data sets by using a least square method, wherein the conversion coefficients are non-uniformity correction coefficients among CCD arrays, and finally, performing secondary correction on the conventional imaging mode image by using the correction coefficients.
2. The method according to claim 1, wherein in step 2,
solving the correction coefficient by using a least square method to make the gray value of a row of pixels formed by a certain probe element to be corrected be { x } i I=1, 2,..r }, the gray average value column of each column of pixels formed by each probe element is { y } i |i＝1,2,...,r}，x i ,y i Within a plane, a set of discrete points { (x) i ,y i ) I=1, 2, & r }, performing multi-point curve fitting on the discrete point set by using the following formula, wherein each coefficient of the fitted curve is a correction coefficient;
linear form fit y=lineara 0 +a 1 The formula for solving the coefficient is x:
quadratic form fitting curve y=a 0 +a 1 x+a 2 x 2 The equation for solving the coefficients is:
wherein r is the number of discrete points; { a 0 ,a 1 ,a 2 And the coefficients of each item of the fitting curve are shown.
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| 多TDICCD拼接相机成像非均匀性实时校正的硬件实现;朱宏殷;郭永飞;司国良;;光学精密工程;20111215(第12期);全文 * |
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