CN113793270A - Aerial image geometric correction method based on unmanned aerial vehicle attitude information - Google Patents
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Abstract
The invention discloses an aerial image geometric correction method based on unmanned aerial vehicle attitude information. According to the method, the geometric correction of the preprocessed unmanned aerial vehicle remote sensing image is realized by using the image internal orientation element of the aerial image and the POS parameter of the aerial image as the image external orientation element directly through three-dimensional reconstruction space coordinate conversion and indirect image correction. The invention fully utilizes the pixel information of the original image, simultaneously participates in the correction of a plurality of attitude parameters, avoids the correction error caused by the fact that a plurality of parameters are not smoothly participated in the correction in the traditional method, carries out geometric correction on the unmanned aerial vehicle aerial image when manual control point pair selection cannot be carried out, leads the image to be more in line with the standard, obtains the corrected image with regularly arranged image points, and is convenient for the subsequent treatments of aerial image registration, splicing and the like.
Description
Technical Field
The invention relates to the field of image processing, in particular to an aerial image geometric correction method based on unmanned aerial vehicle attitude information.
Background
With the rapid development of unmanned aerial vehicles, the low-altitude remote sensing technology of unmanned aerial vehicles gradually becomes an important means for acquiring spatial information. By using a specific processing system, series of operations are carried out on pixels in the aerial image of the unmanned aerial vehicle, and the aims of information processing and information extraction are finally achieved. Up to now, unmanned aerial vehicles have been well applied to various projects such as building measurement, fault detection, wind power inspection and the like.
However, when the unmanned aerial vehicle performs an aerial photography task, the flying motion of the unmanned aerial vehicle may cause the attitude of the imaging sensor of the unmanned aerial vehicle to change due to the influence of external factors such as wind and air current, and the unmanned aerial vehicle shooting platform may have pitching, rolling and yawing conditions, thereby causing pitching deformation, rolling deformation and yawing deformation of the obtained aerial photography image. Due to the existence of these geometric distortions, the images cannot directly represent the shape and planar position of the ground feature, thereby causing geometric distortion of the acquired aerial image. For the same ground object region, the aerial images acquired by different flight attitudes have larger difference, so that the acquired images need to be geometrically corrected to obtain the aerial images based on the same reference projection plane, and the speed of processing the remote sensing image data of the unmanned aerial vehicle and the quality of quantitatively extracted information are improved. The existing unmanned aerial vehicle aerial image geometric correction usually considers manual arrangement of control points for correction, however, although the correction precision of the method is high, a large amount of manpower, material resources and financial resources are required to be invested, particularly, the difficulty in arrangement of the control points in mountainous regions, water surfaces, deserts and the like is extremely high, the method is not suitable for implementation, and in consideration of the fact that part of aerial workers do not have relevant knowledge, relevant distortion exists in data concentration after aerial photography, technical personnel cannot correct the control points through the control point method, and the quality and speed of the geometric correction of the unmanned aerial vehicle aerial image directly influence subsequent data processing and analysis decision-making. Therefore, how to rapidly and accurately eliminate the existing geometric distortion of the original image is a key technology for preprocessing and applying the aerial image of the unmanned aerial vehicle.
Considering that the Inertial Navigation Unit (INU) and the GPS are arranged on the unmanned aerial vehicle, the attitude angle and the coordinates of the unmanned aerial vehicle during navigation can be stored in real time. The method is characterized in that fusion is carried out according to the attitude of an imaging sensor, the aerial photographing time, the auxiliary navigation positioning data and the data acquired by various air sensors in the flight process of the unmanned aerial vehicle, image correction without a control point or an ortho-reference is completed, and the method is easy to realize in practical application. In order to enable the unmanned aerial vehicle remote sensing data to be rapidly and effectively applied to information extraction and analysis, a corresponding processing model needs to be established, and geometric correction of the unmanned aerial vehicle remote sensing image can be completed under the condition of lacking a ground control point. The essence of image correction is to implement geometric transformation between two-dimensional images, therefore, before image correction, a geometric relationship between an original image and a corrected image needs to be established, two methods, namely a direct method and an indirect method, are usually adopted for correction, and li zheng proposes a method for image geometric correction only by using aerial flight information (lei zheng, "research on geometric correction technology of unmanned aerial vehicle remote sensing image lacking control points," university of electronic technology, 2010), namely, a method for calculating image plane coordinates of corresponding image points on the corrected image one by one through a geometric relationship established by attitude information according to image plane coordinates of each image point on the original image by a direct method, but the image points on the image corrected by the method are irregularly arranged, blank or repeated pixels may occur, and interpolation of pixel values is difficult to a certain extent, therefore, it is difficult to obtain a corrected image with regularly arranged pixels.
Disclosure of Invention
Because the direct method has the defects, the authenticity of original information of an aerial image is considered, the invention can know the internal orientation elements of the image according to a camera calibration report under the condition of no ground control point, namely the coordinate of an image main point in a frame mark coordinate system and the camera focal length, simultaneously, the POS parameters of the aerial image are directly used as the external orientation elements of the image, the geometric correction of the preprocessed unmanned aerial vehicle remote sensing image is realized through three-dimensional reconstruction space coordinate conversion and indirect image correction, and the plane of the unmanned aerial vehicle remote sensing image with geometric deformation is converted into the ground horizontal plane.
In the implementation process of the method, the scenery is imaged on the camera sensor to form a two-dimensional image. Note that the coordinates of the image plane of a certain point in space on the corrected image are (u ', v'), and the coordinates of the image plane on the original image are (u, v).
In order to achieve the purpose, the invention provides the following technical scheme:
step 1: physical equivalent coordinates corresponding to corrected image coordinates are obtained
Calculating a physical length (d) of a unit pixel from camera parametersx,dy) According to the physical length and the camera focal length f, solving an internal parameter matrix K1And the corrected image point and the world coordinate system coordinate have no rotation and translation distortion, the physical equivalent coordinate corresponding to the corrected image point (u ', v') is (X, Y, Z), the physical equivalent coordinate refers to the coordinate value of the physical coordinate system divided by the coordinate of the aircraft flight height, and is expressed by the formula:
step 2: calculating the coordinates of the corrected image plane corresponding to the physical equivalent coordinates
Calculating the coordinates of an image plane before correction according to the physical equivalent coordinates and flight attitude parameters obtained in the step 1 and the following formula;
wherein R is a rotation matrix obtained according to flight attitude parameters, and T is a translation vector;
and step 3: calculating the corrected image
For each corrected image point coordinate (u ', v'), obtaining the image point coordinate (u, v) of the image before correction according to the coordinate transformation relation obtained in the steps 1 and 2; obtaining a pixel value with coordinates (u ', v') in the corrected image by interpolating the pixels of the image before correction;
and 4, step 4: selecting proper resampling method to carry out gray scale assignment on output image pixel
And (3) for each corrected image point coordinate (u ', v'), obtaining a pixel coordinate (u, v) of the image before correction according to the coordinate transformation relation obtained in the steps (1) and (2), resampling the corrected image by adopting an interpolation method to obtain a pixel value with the coordinate (u ', v') in the corrected image, wherein the interpolation method comprises a nearest neighbor interpolation method, a bilinear interpolation method and a cubic convolution interpolation method.
Further, the moments of intrinsic parameters described in Steps 1 and 2Matrix K1Is calculated by the formula
Wherein (C)x,Cy) The coordinates of the optical center of the image under the coordinates of image pixels are shown, dx and dy respectively show the size of unit pixels on the x-axis and y-axis of the sensor, and f is the focal length of the camera.
Further, the calculation formula of the rotation matrix R in step 2 is:
wherein,
theta and omega are the pitch angle, roll angle and yaw angle in the image attitude parameters, respectively.
The invention has the beneficial effects that:
(1) the mapping relation before and after correction is established by using the geographical attitude information of the remote sensing image, the unmanned aerial vehicle aerial image is geometrically corrected when the manual control point pair selection cannot be carried out, so that the image is more in line with the standard, the corrected image with regularly arranged image points is obtained, and subsequent aerial image registration, splicing and other processing are facilitated.
(2) And a plurality of attitude parameters participate in correction at the same time, so that correction errors caused by the fact that a plurality of parameters do not participate in correction smoothly in the traditional method are avoided.
Drawings
FIG. 1 is a schematic diagram comparing the direct method and indirect method
FIG. 2 is a graph of an image correction by an indirect method
FIG. 3(a) is an aerial image of a raw assembly;
FIG. 3(b) is a corrected component image
FIG. 4(a) is an aerial image of a raw assembly;
FIG. 4(b) is a detail image of the corrected assembly
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail below with reference to specific embodiments and accompanying drawings, and the specific embodiments are described to simplify the present invention. It is to be understood that the invention is not limited to the embodiments described and that various modifications thereof are possible without departing from the basic concept, and such equivalents are intended to fall within the scope of the invention as defined in the appended claims.
As shown in fig. 1 and 2, a geometric correction method based on attitude information of an aerial image of an unmanned aerial vehicle is specifically implemented as follows:
step 1: physical equivalent coordinates corresponding to corrected image coordinates are obtained
Calculating a physical length (d) of a unit pixel from camera parametersx,dy) According to the physical length and the camera focal length f, solving an internal parameter matrix K1And the corrected image point and the world coordinate system coordinate have no rotation and translation distortion, the physical equivalent coordinate corresponding to the corrected image point (u ', v') is (X, Y, Z), the physical equivalent coordinate refers to the coordinate value of the physical coordinate system divided by the coordinate of the aircraft flight height, and then the coordinate conversion formula is as follows:
step 2: calculating the coordinates of the corrected image plane corresponding to the physical equivalent coordinates
Calculating the coordinates of an image plane before correction according to the physical equivalent coordinates and flight attitude parameters obtained in the step 1 and the following formula;
wherein R is a rotation matrix obtained according to flight attitude parameters, and T is a translation vector;
and step 3: selecting a proper resampling method to perform gray scale assignment on the output image pixel;
and (3) for each corrected coordinate (u ', v'), obtaining the pixel coordinate (u, v) of the image before correction according to the coordinate transformation relation obtained in the steps (1) and (2), and completing the mapping from the pixel coordinate of the image after correction to the pixel coordinate of the image before correction. And after the coordinate transformation is finished, resampling the corrected image by adopting an interpolation method to obtain a pixel value with the coordinate (u ', v') in the corrected image.
The interpolation method is realized by taking out the gray value on the original image point location calculated by the steps and filling the gray value back to the corresponding pixel point location in the blank output image dot matrix. Since the pixel is not necessarily located at a certain pixel center of the original image, gray scale interpolation is required for this purpose, and generally, three methods, namely nearest neighbor interpolation, bilinear interpolation and cubic convolution interpolation, are commonly used. And (4) carrying out interpolation on the pixels of the image before correction to obtain the pixel values of the coordinates corresponding to the corrected image, thus finishing the image correction.
Further, the internal parameter matrix K of steps 1 and 21Is calculated by the formula
Wherein (C)x,Cy) The coordinates of the optical center of the image under the coordinates of image pixels are shown, dx and dy respectively show the size of unit pixels on the x-axis and y-axis of the sensor, and f is the focal length of the camera.
Further, the calculation formula of the rotation matrix R in step 2 is:
wherein,
theta and omega are the pitch angle, roll angle and yaw angle in the image attitude parameters, respectively.
According to the method, the geometric correction of the preprocessed unmanned aerial vehicle remote sensing image is realized by using the image internal orientation element of the aerial image and the POS parameter of the aerial image as the image external orientation element directly through three-dimensional reconstruction space coordinate conversion and indirect image correction. The invention fully utilizes the pixel information of the original image, simultaneously participates in the correction of a plurality of attitude parameters, avoids the correction error caused by the fact that a plurality of parameters are not smoothly participated in the correction in the traditional method, carries out geometric correction on the unmanned aerial vehicle aerial image when manual control point pair selection cannot be carried out, leads the image to be more in line with the standard, obtains the corrected image with regularly arranged image points, and is convenient for the subsequent treatments of aerial image registration, splicing and the like.
The specific implementation process of the invention is as follows:
the photovoltaic module aerial photography picture that will adopt unmanned aerial vehicle to acquire picks out the image that wherein angle offset is slightly big, as shown in fig. 3(a), carries out geometric correction to it, and relevant image acquisition parameter is shown as table 1.1:
TABLE 1.1 image acquisition parameter Table
The image correction process is as follows:
step 1: physical equivalent coordinates corresponding to corrected image coordinates are obtained
The intersection of the optical axis of the camera and the image plane, usually at the image center, is calculated as the image optical center coordinate CxAnd Cy：
The width F of the sensor can be known by looking up a table according to the size of the sensorw7.4mm, high FhIs 5.6mm, and calculates the size d of unit pixel on x-axis and y-axis of the sensorx＝ Fw/W,dy＝Fh/H。
Calculating an internal parameter matrix K according to the formula (3)1The physically equivalent coordinates are calculated pixel by pixel according to equation (1).
Step 2: calculating the coordinates of the image plane before correction corresponding to the physical equivalent coordinates
The pitch angle in the image attitude parameters
Substituting the roll angle theta and the yaw angle omega into a formula (4), calculating to obtain a rotation matrix R, and setting a translation vector T to be [ 000 ]]-1The image plane coordinates before correction are calculated pixel by pixel according to formula (2).
And step 3: calculating the corrected image
For each corrected coordinate (u ', v'), obtaining the pixel coordinate (u, v) of the image before correction according to the coordinate transformation relation obtained in the steps 1 and 2, and resampling the corrected image by using a bilinear interpolation method to obtain the pixel value with the coordinate (u ', v') in the corrected image, wherein the result is shown in fig. 3 (b). The image details are enlarged to obtain the detail comparison before and after correction, for example, as shown in fig. 4(a) and fig. 4(b), the shadow part of the component is covered by the rectangular mask with the same size, and the superposition rate of the corrected shadow part of the component and the mask is higher, and the outline of the photovoltaic component has more geometric characteristics.
Claims (4)
1. An aerial image geometric correction method based on unmanned aerial vehicle attitude information is characterized by comprising the following steps:
step 1: physical equivalent coordinates corresponding to corrected image coordinates are obtained
Calculating a physical length (d) of a unit pixel from camera parametersx,dy) According to the physical length and the camera focal length f, solving an internal parameter matrix K1And the corrected image point and the world coordinate system coordinate have no rotation and translation distortion, the physical equivalent coordinate corresponding to the corrected image point (u ', v') is (X, Y, Z), the physical equivalent coordinate refers to the coordinate value of the physical coordinate system divided by the coordinate of the aircraft flight height, and is expressed by the formula:
step 2: calculating the coordinates of the corrected image plane corresponding to the physical equivalent coordinates
Calculating the coordinates of an image plane before correction according to the physical equivalent coordinates and flight attitude parameters obtained in the step 1 and the following formula;
wherein R is a rotation matrix obtained according to flight attitude parameters, and T is a translation vector;
and step 3: selecting a proper resampling method to perform gray scale assignment on the output image pixel;
for each corrected coordinate (u ', v'), obtaining a pixel coordinate (u, v) of the image before correction according to the coordinate transformation relation obtained in the steps 1 and 2, and finishing mapping from the pixel coordinate of the image after correction to the pixel coordinate of the image before correction; and after the coordinate transformation is finished, resampling the corrected image by adopting an interpolation method to obtain a pixel value with coordinates (u ', v') in the corrected image, wherein the interpolation method comprises a nearest neighbor interpolation method, a bilinear interpolation method and a cubic convolution interpolation method.
2. The method for geometrically correcting attitude information based on aerial images of unmanned aerial vehicles according to claim 1, wherein the internal parameter matrix K in steps 1 and 21Is calculated by the formula
Wherein (C)x,Cy) The coordinates of the optical center of the image under the coordinates of image pixels are shown, dx and dy respectively show the size of unit pixels on the x-axis and y-axis of the sensor, and f is the focal length of the camera.
3. The geometric correction method based on unmanned aerial vehicle aerial image attitude information according to claim 1 or 2, characterized in that the rotation matrix R in step 2 is calculated by the following formula:
wherein,
theta and omega are the pitch angle, roll angle and yaw angle in the image attitude parameters, respectively.
4. The geometric correction method based on the attitude information of the aerial image of the unmanned aerial vehicle according to claim 1 or 2, characterized in that the interpolation method is realized by taking out the gray value calculated by the steps on the original image point location and filling the gray value back to the corresponding pixel point location in the blank output image dot matrix; because the image correction method is not exactly positioned at the center of a certain pixel of the original image, the gray value of the pixel is determined by gray interpolation, a proper interpolation method can be selected to perform gray assignment on the pixel of the output image of the distorted image, and the pixel value of the corresponding coordinate of the corrected image is obtained by interpolating the pixel of the image before correction, so that the image correction can be completed.
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