CN118096615A - Satellite image processing method, device, medium and electronic equipment - Google Patents
Satellite image processing method, device, medium and electronic equipment Download PDFInfo
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Abstract
The application provides a satellite image processing method, a satellite image processing device, a satellite image processing medium and electronic equipment. The satellite image processing method comprises the following steps: acquiring a satellite image pair and an associated initial RPC parameter thereof; based on the initial RPC parameters, carrying out binding adjustment optimization processing on the matching point pairs in the satellite image pair to obtain an Euler angle required to be optimized for a camera associated with the satellite image pair, an optimized translation vector and an optimized 3D point of the matching point pair, wherein an objective function value in the binding adjustment optimization processing is determined by a reprojection error value and a translation correction value under a view angle of the camera associated with the satellite image pair, and the translation correction value consists of a translation correction factor and the Euler angle to be optimized; and acquiring corrected RPC parameters based on the optimized Euler angle, the optimized translation vector, the matching point pair and the optimized 3D point. The satellite image processing method can improve the accuracy of RPC parameters.
Description
Technical Field
The application belongs to the field of remote sensing image processing, relates to a satellite image processing method, and in particular relates to a satellite image processing method, a device, a medium and electronic equipment.
Background
In recent years, satellite remote sensing earth observation technology is rapidly developed, so that the satellite remote sensing earth observation technology is widely applied to the aspects of environment monitoring, urban planning, navigation positioning and the like. At present, remote sensing images acquired by earth observation satellites are two-dimensional information carriers, and traditional two-dimensional images lack three-dimensional information of an observation area, so that the development requirements of national economy aspects such as smart city planning and the like cannot be met. The traditional method for measuring the elevation information mostly adopts an airborne radar height measurement system, but the action range is limited. Compared with ground image acquisition equipment, the satellite image can cover a wider area; compared with active devices such as radar, satellite images are convenient to collect, and geometric shape and texture information of a certain area can be obtained simultaneously.
The traditional three-dimensional reconstruction method based on satellite remote sensing images mainly utilizes image pair images acquired by a multi-linear array camera of an earth observation satellite, the number of earth observation satellites for carrying the multi-linear array camera in China is small at present, and the acquisition of low-cloud cover remote sensing images at a specific place at any time is difficult according to actual requirements. In recent years, the number of high-resolution optical remote sensing satellites is continuously increased, and the data acquisition capacity is continuously enhanced, so that the earth observation data volume is large, and a plurality of satellite images with different dates can be shot in a certain area. Therefore, it is practical and necessary to use satellite remote sensing images of different dates to reconstruct three-dimensional images of a certain area without being limited to the image pair images acquired by the multi-linear-array camera.
The accuracy of RPC parameters in the satellite image reconstruction technology influences the accuracy of satellite image reconstruction, and the problem of poor accuracy of the RPC parameters exists in the current satellite image processing method.
Disclosure of Invention
The application aims to provide a satellite image processing method, a device, a medium and electronic equipment, which are used for solving the problem of poor RPC parameter precision existing in the conventional satellite image processing method.
In a first aspect, the present application provides a satellite image processing method, including: acquiring a satellite image pair and an associated initial RPC parameter thereof; based on the initial RPC parameters, carrying out binding adjustment optimization processing on the matching point pairs in the satellite image pair to obtain an Euler angle required to be optimized for a camera associated with the satellite image pair, an optimized translation vector and an optimized 3D point of the matching point pair, wherein an objective function value in the binding adjustment optimization processing is determined by a reprojection error value and a translation correction value under a view angle of the camera associated with the satellite image pair, and the translation correction value consists of a translation correction factor and the Euler angle to be optimized; and acquiring corrected RPC parameters based on the optimized Euler angle, the optimized translation vector, the matching point pair and the optimized 3D point.
In the satellite image processing method, the optimized Euler angle, the optimized translation vector and the optimized 3D point can be obtained according to the objective function value in the binding adjustment process determined by the re-projection error value and the translation correction value together, so that the corrected RPC parameter can be obtained.
In an embodiment of the present application, based on the initial RPC parameter, a binding adjustment optimization process is performed on a matching point pair in the satellite image pair to obtain an euler angle, an optimized translation vector, and an optimized 3D point of the matching point pair, which are required to be optimized for a camera associated with the satellite image pair, where the implementation method includes: performing feature extraction and matching processing on the satellite image pair to obtain a matching point pair in the satellite image pair; triangulating the matching point pairs based on the initial RPC parameters to obtain initial 3D points of the matching point pairs; based on the initial 3D point, the matching point pair, a rotation matrix to be optimized, a translation vector to be optimized, the translation correction value and the objective function under a camera view angle related to the satellite image pair, the optimized euler angle, the optimized translation vector and the optimized 3D point are obtained, wherein the optimized euler angle, the optimized translation vector and the optimized 3D point are euler angles, translation vectors and 3D points related to the objective function value which is better under the camera view angle.
In one embodiment of the present application, the rotation matrix at the mth camera view angle is expressed as:
Rm＝Rx(αm)Ry(βm)Rz(γm)
Wherein, R m represents a rotation matrix under the view angle of the mth camera, R x (DEG) represents rotation on the x axis, R y (DEG) represents rotation on the y axis, R z (DEG) represents rotation on the z axis, alpha m represents a first angle, beta m represents a second angle, gamma m represents a third angle, alpha m、βm and gamma m form an Euler angle to be optimized of the mth camera, and m is a positive integer not less than 1.
In an embodiment of the present application, the satellite image processing method further includes: performing stereo correction processing on the satellite image pair based on the corrected RPC parameters to obtain the stereo corrected satellite image pair, and performing stereo matching processing on the corrected satellite image pair to obtain a parallax image after optimization of the corrected satellite image pair; and acquiring a digital earth surface model and a three-dimensional point cloud model with geographic position and elevation information based on the optimized parallax map and the corrected RPC parameters.
In an embodiment of the present application, the implementation method for performing stereo correction processing on the satellite image pair based on the corrected RPC parameter to obtain the stereo corrected satellite image pair includes: acquiring geographic coordinates and elevations of an area in a first satellite image based on a satellite positioning model associated with the first satellite image, wherein the first satellite image is one image of the satellite image pair, and the satellite positioning model is related to the corrected RPC parameter associated with the first satellite image; acquiring pseudo-matching points of the first satellite image based on the geographic coordinates, the elevation and a satellite projection model associated with a second satellite image, wherein the second satellite image is the other image in the satellite image pair, and the satellite projection model associated with the second satellite image is related to the corrected RPC parameter associated with the second satellite image; acquiring an image transformation matrix based on the pseudo-matching point pairs, the geographic coordinates and the elevation; and performing image transformation processing on the second satellite image based on the image transformation matrix to acquire the stereoscopic corrected satellite image pair.
In an embodiment of the present application, the implementation method for performing stereo matching processing on the corrected satellite image pair to obtain the optimized disparity map of the corrected satellite image pair includes: acquiring a parallax map of the stereoscopic corrected satellite image pair based on the matching cost and the star path of the stereoscopic corrected satellite image pair, wherein the pixel parallax in the parallax map is a parallax value associated with the minimum aggregation cost of the pixels; and carrying out optimization processing on the parallax map to obtain an optimized parallax map.
In an embodiment of the present application, the implementation method for obtaining the digital earth surface model and the three-dimensional point cloud model with the geographic position and the elevation information based on the optimized disparity map and the corrected RPC parameters includes: and carrying out triangulation on the optimized parallax map based on the corrected RPC parameters so as to acquire the digital surface model and the three-dimensional point cloud model.
In a second aspect, the present application provides a satellite image processing apparatus comprising: the image pair acquisition module is used for acquiring a satellite image pair and an associated initial RPC parameter; the camera parameter acquisition module is used for carrying out binding adjustment optimization processing on the matching point pairs in the satellite image pair based on the initial RPC parameters so as to acquire an Euler angle required to be optimized for a camera associated with the satellite image pair, an optimized translation vector and an optimized 3D point of the matching point pair, wherein an objective function value in the binding adjustment optimization processing is determined by a reprojection error value and a translation correction value under a view angle of the camera associated with the satellite image pair, and the translation correction value consists of a translation correction factor and the Euler angle to be optimized; and the correction parameter acquisition module is used for acquiring corrected RPC parameters based on the optimized Euler angle, the optimized translation vector, the matching point pair and the optimized 3D point.
In a third aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the satellite image processing method according to any one of the first aspects of the present application.
In a fourth aspect, the present application provides an electronic device, including: a memory storing a computer program; and the processor is in communication connection with the memory and executes the satellite image processing method according to any one of the first aspect of the application when the computer program is called.
As described above, the satellite image processing method, the device, the medium and the electronic equipment have the following beneficial effects:
In the satellite image processing method, the optimized Euler angle, the optimized translation vector and the optimized 3D point can be obtained according to the objective function value in the binding adjustment process determined by the re-projection error value and the translation correction value together, so that the corrected RPC parameter can be obtained.
Drawings
Fig. 1 is a schematic diagram of a hardware structure for running the satellite image processing method according to an embodiment of the application.
Fig. 2 is a flowchart of a satellite image processing method according to an embodiment of the application.
Fig. 3 is a flowchart of an implementation method of binding adjustment optimization processing on a matching point pair in the satellite image pair based on the initial RPC parameter to obtain an euler angle, an optimized translation vector, and an optimized 3D point of the matching point pair required to be optimized for a camera associated with the satellite image pair according to an embodiment of the present application.
FIG. 4 is a schematic diagram showing feature matching according to an embodiment of the present application.
Fig. 5 is a flowchart of an implementation method of the satellite image processing method according to an embodiment of the application.
Fig. 6 is a flowchart of an implementation method for performing stereo correction processing on the satellite image pair based on the corrected satellite projection mapping model parameters to obtain a stereo corrected satellite image pair according to an embodiment of the present application.
Fig. 7 is a schematic diagram of the embodiment of the application before and after the stereo correction of the location 1.
Fig. 8 is a flowchart of an implementation method of performing stereo matching processing on the corrected satellite image pair to obtain the optimized disparity map of the corrected satellite image pair according to an embodiment of the present application.
FIG. 9 is a schematic diagram of a digital earth model at site 1 according to an embodiment of the present application.
Fig. 10 is a schematic diagram of a three-dimensional point cloud model of a location 1 according to an embodiment of the present application.
Fig. 11 is a schematic structural diagram of a satellite image processing apparatus according to an embodiment of the application.
Description of element reference numerals
10. Electronic equipment
110. Memory device
120. Processor and method for controlling the same
130. Bus line
140. Access device
150. Database for storing data
1100. Satellite image processing device
1110. Image pair acquisition module
1120. Camera parameter acquisition module
1130. Correction parameter acquisition module
S11-S13 step
S21-S23 step
S31-S33 step
S41-S44 step
S51-S52 steps
Detailed Description
Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present application by way of illustration, and only the components related to the present application are shown in the illustrations, not according to the number, shape and size of the components in actual implementation, and the form, number and proportion of each component in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
The following describes the technical solution in the embodiment of the present application in detail with reference to the drawings in the embodiment of the present application.
The satellite image processing method provided by the embodiment of the application can be operated in the computing equipment. Taking fig. 1 as an example, fig. 1 is a block diagram of a hardware structure of a computing device running the satellite image processing method. Computing device 10 includes, but is not limited to, memory 110 and processor 120. Processor 120 is coupled to memory 110 via bus 130 and database 150 is used to store data.
Computing device 10 also includes access device 140, access device 140 enabling computing device 10 to communicate via one or more networks 160. Examples of such networks include public switched telephone networks, local area networks, wide area networks, personal area networks, or combinations of communication networks such as the internet. The access device 140 may include any type of network interface, wired or wireless, for example, one or more of network interface cards, such as an IEEE802.11 wireless local area network wireless interface, a worldwide interoperability for microwave access interface, an ethernet interface, a universal serial bus interface, a cellular network interface, a bluetooth interface, a near-field communication interface, and so forth.
In embodiments of the present application, the above-described components of computing device 10, as well as other components not shown in FIG. 1, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device shown in FIG. 1 is for exemplary purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as desired.
Computing device 10 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a laptop, notebook, netbook, etc.) or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 10 may also be a mobile or stationary server.
As shown in fig. 2, the present embodiment provides a satellite image processing method, which includes:
S11, acquiring an initial RPC parameter of a satellite image pair and related to the satellite image pair.
Optionally, the satellite image pair may refer to two satellite images with acquisition angle difference between 5 ° and 30 ° acquired at the same geographic location, the satellite image pair may be formed by extracting a region of interest in an original satellite image with different viewing angles through a clipping manner, and the satellite image may refer to a satellite image with two-dimensional information as a carrier. The region of interest refers to the region of interest of the image in the image processing field, and corresponds to the geographical region of interest of the satellite image.
Optionally, each satellite image in the satellite image pair has an associated set of RPC (Rational Polynomial Coefficient, rational polynomial coefficients) parameters that are used to describe the satellite projection mapping model. The initial RPC parameters may refer to the initial RPC parameters associated with each satellite image in the satellite image pair.
And S12, carrying out binding adjustment optimization processing on the matching point pairs in the satellite image pair based on the initial RPC parameters so as to obtain an Euler angle required to be optimized for a camera associated with the satellite image pair, an optimized translation vector and an optimized 3D point of the matching point pair, wherein an objective function value in the binding adjustment optimization processing is determined by a reprojection error value and a translation correction value under the view angle of the camera associated with the satellite image pair, and the translation correction value consists of a translation correction factor and the Euler angle to be optimized.
Alternatively, the translation correction factor may be flexibly set according to practical situations, which is not explicitly limited in this embodiment. The optimized 3D point may be referred to as optimized 3D point coordinates.
Optionally, the binding adjustment optimization process may refer to nonlinear optimization of the three-dimensional point positions of the matching point pair and the camera parameters associated with the satellite image pair, and the binding adjustment optimization process may also be regarded as a process of minimizing the re-projection error of the matching point pair. The euler angle is a camera parameter in the binding adjustment optimization process, the re-projection error can be a two-dimensional estimated coordinate of the three-dimensional coordinate associated with the matching point pair projected in the satellite image, and an error between the two-dimensional estimated coordinate and a two-dimensional actual coordinate of the matching point in the satellite image is the re-projection error. The objective function in the binding adjustment optimization process may be referred to as a nonlinear optimized objective function. The optimized euler angles and the optimized translation vectors can be used for compensating the deviation of RPC parameters under the view angle of a camera, the RPC parameters are used for describing a satellite projection mapping model, the satellite projection mapping model is mainly represented by RFM (Rational Function Model, a rational function model), and the RFM model can directly establish the relation between image points and space coordinates without physical model information of sensor imaging.
S13, acquiring corrected RPC parameters based on the optimized Euler angle, the optimized translation vector, the matching point pair and the optimized 3D point.
Optionally, the implementation method for obtaining the corrected RPC parameters based on the optimized euler angle, the optimized translation vector, the matching point pair and the optimized 3D point includes: and calculating and acquiring the corrected RPC parameters by a least squares fitting method based on the optimized Euler angle, the optimized translation vector, the optimized 3D point and the matching point pair. The optimized 3D point may refer to optimized 3D point coordinates, the matching point pair may refer to coordinates of a matching point pair, and the optimized 3D point coordinates are relatively accurate compared to the coordinates of the initial 3D point of the matching point pair.
As can be seen from the above description, the satellite image processing method according to the present embodiment includes: acquiring a satellite image pair and an associated initial RPC parameter thereof; based on the initial RPC parameters, carrying out binding adjustment optimization processing on the matching point pairs in the satellite image pair to obtain an Euler angle required to be optimized for a camera associated with the satellite image pair, an optimized translation vector and an optimized 3D point of the matching point pair, wherein an objective function value in the binding adjustment optimization processing is determined by a reprojection error value and a translation correction value under a view angle of the camera associated with the satellite image pair, and the translation correction value consists of a translation correction factor and the Euler angle to be optimized; and acquiring corrected RPC parameters based on the optimized Euler angle, the optimized translation vector, the matching point pair and the optimized 3D point.
In the satellite image processing method, the optimized Euler angle, the optimized translation vector and the optimized 3D point can be obtained according to the objective function value in the binding adjustment process determined by the re-projection error value and the translation correction value together, so that the corrected RPC parameter can be obtained.
As shown in fig. 3, the implementation method for performing binding adjustment optimization processing on the matching point pair in the satellite image pair based on the initial RPC parameter to obtain the euler angle, the optimized translation vector and the optimized 3D point of the matching point pair required to be optimized for the camera associated with the satellite image pair includes:
And S21, performing feature extraction and matching processing on the satellite image pair to acquire a matching point pair in the satellite image pair.
Optionally, the implementation method for performing feature extraction and matching processing on the satellite image pair comprises the following steps: and performing SIFT (SCALE INVARIANT Feature Transform ) feature extraction processing on the target region, and performing feature matching processing according to a distance ratio method and a RANSAC (Random Sample Consensus, random sampling consistency) algorithm to obtain the matching point pairs, wherein a feature matching processing schematic diagram can be shown in fig. 4.
And S22, performing triangulation on the matching point pair based on the initial RPC parameter to acquire an initial 3D point of the matching point pair.
Alternatively, the specific values of the initial RPC parameters are not specifically limited in this embodiment, and the initial RPC parameters are provided by the satellite company together with the satellite image. When the satellite image is reconstructed, the error of the reconstructed three-dimensional point is increased, and the error of the RPC parameter comes from inaccurate record of the satellite pose.
Alternatively, the initial 3D point may be expressed as: o 0＝(X0,Y0,Z0).
Optionally, each of the satellite images has an associated satellite projection mapping model parameter, which may be an RPC parameter. The satellite projection mapping model may be simplified to be represented as o=p (O), where o= (X, Y), o= (X, Y, Z), the specific form of P (·) is as follows:
NumL(X,Y,Z)＝a0+a1Y+a2X+a3Z+a4YX+a5YZ+a6XZ+a7Y2+a8X2+a9Z2+a10XYZ+a11Y3+a12YX2+a13YZ2+a14Y2X+a15X3+a16XZ2+a17Y2Z+a18X2Z+a19Z3
DenL(X,Y,Z)＝b0+b1Y+a2X+b3Z+b4YX+b5YZ+b6XZ+b7Y2+b8X2+b9Z2+b10XYZ+b11Y3+b12YX2+b13YZ2+b14Y2X+b15X3+b16XZ2+b17Y2Z+b18X2Z+b19Z3
NumS(X,Y,Z)＝e0+e1Y+e2X+e3Z+e4YX+e5YZ+e6XZ+e7Y2+e8X2+e9Z2+e10XYZ+e11Y3+e12YX2+e13YZ2+e14Y2X+e15X3+e16XZ2+e17Y2Z+e18X2Z+e19Z3
DenS(X,Y,Z)＝f0+f1Y+f2X+f3Z+f4YX+f5YZ+f6XZ+f7Y2+f8X2+f9Z2+f10XYZ+f11Y3+f12YX2+f13YZ2+f14Y2X+f15X3+f16XZ2+f17Y2Z+f18X2Z+f19Z3
Where X represents the normalized image line value, Y represents the normalized image column value, X represents the normalized geographic longitude, Y represents the normalized latitude, Z represents the normalized elevation coordinate, and the normalization serves to minimize the numerical error. The image coordinates are in pixels, and the ground coordinates are in decimal longitude, latitude, and elevation in meters. The normalization formula is as follows:
The LAT OFF、LATSCALE、LONGOFF、LONGSCALE、HEIGHTOFF、HEIGHTSCALE is a normalized parameter of a ground coordinate, the LINE OFF、LINESCALE、SAMPOFF、SAMPSCALE is a normalized parameter of an image coordinate, a i、bi、ei、fi is the RPC parameter, and typically, there are 90 parameters in the RPC file.
S23, acquiring the optimized Euler angle, the optimized translation vector and the optimized 3D point based on the initial 3D point, the matching point pair, a rotation matrix to be optimized, a translation vector to be optimized, the translation correction value and the objective function under the camera view angle related to the satellite image pair, wherein the optimized Euler angle, the optimized translation vector and the optimized 3D point are Euler angles, translation vectors and 3D points related to the objective function value which is better under the camera view angle.
Optionally, the rotation matrix may be initially a unit matrix, the translation vector may be initially a unit vector, and the rotation matrix and the translation vector may correct an error of Wei Xingwei pose, so as to compensate for a deviation of the RPC parameter.
Optionally, the rotation matrix at the mth camera view angle is expressed as:
Rm＝Rx(αm)Ry(βm)Rz(γm)
Wherein, R m is expressed as a rotation matrix under the view angle of the mth camera, R x (DEG) is expressed as rotation on the x axis, R y (DEG) is expressed as rotation on the y axis, R z (DEG) is expressed as rotation on the z axis, alpha m is expressed as a first angle, beta m is expressed as a second angle, gamma m is expressed as a third angle, alpha m、βm and gamma m form an Euler angle to be optimized of the mth camera, and m is a positive integer not less than 1.
Alternatively, the objective function may be expressed as:
preferably, the objective function value may be minL, which may be expressed as:
Wherein O mk represents the K-th pair of matching point coordinates on the satellite image at the M-th camera view angle, O k represents the three-dimensional point coordinates corresponding to O mk, K represents the number of matching point pairs on the satellite image, M represents the satellite image having M camera views in total, P m represents the RPC projection map at the M-th camera view angle, t m represents the translation vector for compensating the RPC error at the M-th camera view angle, R m represents the rotation matrix at the M-th camera view angle, λ represents the translation correction factor, α m represents the first angle at the M-th camera view angle, β m represents the second angle at the M-th camera view angle, and γ m represents the third angle at the M-th camera view angle. The objective function may be solved by an LM (Levenberg-Marquard ) algorithm to obtain the optimized euler angles and the optimized translation vectors. The re-projection error before the translational optimization of the binding adjustment is about 1.94 pixels, and the re-projection error after the translational optimization of the binding adjustment is smaller, which is about 0.06 pixels. The euler angle and the translation vector associated with the preferred objective function value can be just the euler angle and the translation vector when the value of the objective function L is at the minimum, that is, minL. Wherein (O k,αm,βm,γm,tm) represents the coordinates of the optimized 3D point, the optimized euler angle, and the optimized translation vector.
Alternatively, o mk-Pm(RmOk+tm) may represent the reprojection error values described above,The translational correction values described above may be represented.
As shown in fig. 5, the present embodiment provides a satellite processing method, where the satellite processing method further includes:
and S31, carrying out stereo correction processing on the satellite image pair based on the corrected satellite projection mapping model parameters so as to acquire the satellite image pair after stereo correction.
Alternatively, the stereo corrected satellite image pair refers to a satellite image pair subjected to geometric stereo correction. The geometric stereo correction process may refer to a process of transforming the satellite image pair such that epipolar lines of the satellite image pair are parallel and aligned in a horizontal direction.
S32, performing stereo matching processing on the corrected satellite image pair to obtain a parallax image after optimization of the corrected satellite image pair.
And S33, acquiring a digital earth surface model and a three-dimensional point cloud model with geographic position and elevation information based on the optimized parallax map and the corrected RPC parameters.
As shown in fig. 6, the present embodiment provides a method for implementing stereo correction processing on the satellite image pair based on the corrected RPC parameter to obtain the stereo corrected satellite image pair, including:
S41, acquiring geographic coordinates and elevations of an area in a first satellite image based on a satellite positioning model associated with the first satellite image, wherein the first satellite image is one image in the satellite image pair, and the satellite positioning model is related to a corrected RPC parameter associated with the first satellite image.
Alternatively, the geographic coordinates and elevations are represented by O above,Where o= (X, Y, Z), O 1＝(x1,y1), where O 1 represents a two-dimensional point on the satellite image, and Z 0 represents an elevation value initial value, typically set to 1,/>And representing a satellite positioning model associated with the first satellite image, wherein the satellite positioning model is a satellite back projection mapping model. The region in the first satellite image is the region of interest described above.
S42, based on the geographic coordinates, the elevation and a satellite projection model associated with a second satellite image, obtaining pseudo-matching points of the first satellite image, wherein the second satellite image is the other image in the satellite image pair, and the satellite projection model associated with the second satellite image is related to the correction RPC parameter associated with the second satellite image.
Optionally, the pseudo-matching point is denoted as O 2＝P2 (O), where O 2 is the pseudo-matching point of O 1, and P 2 represents the satellite projection model associated with the second satellite image.
S43, acquiring an image transformation matrix based on the pseudo-matching point pairs, the geographic coordinates and the elevation.
Optionally, the pair of pseudo-matching points may be (o 1,o2) as described above.
Optionally, the implementation method for acquiring the image transformation matrix based on the pseudo-matching point pair, the geographic coordinates and the elevation comprises the following steps: and processing the pseudo matching point pairs, the geographic coordinates and the elevation by a gold standard algorithm to obtain the image transformation matrix. The image transformation matrix may be represented as a basis matrix F for image transformation of the satellite image pair to a corrected stereoscopic satellite image pair.
And S44, performing image transformation processing on the second satellite image based on the image transformation matrix to acquire the stereoscopic corrected satellite image pair.
Optionally, the specific process of performing the image transformation processing on the satellite image according to the image transformation matrix is not described herein. Taking the place 1 as an example, a schematic diagram before and after correction is shown in fig. 7.
As shown in fig. 8, the embodiment provides a method for implementing stereo matching processing on the corrected satellite image pair to obtain an optimized disparity map of the corrected satellite image pair, including:
S51, acquiring a disparity map of the stereoscopic corrected satellite image pair based on the matching cost and the star path of the stereoscopic corrected satellite image pair, wherein the pixel disparity in the disparity map is a disparity value associated with the minimum aggregation cost of the pixels.
Optionally, the matching cost may be obtained by performing Census transformation calculation on the stereo corrected satellite image pair, the minimum aggregation cost may be obtained by performing matching cost aggregation calculation according to two vertical directions selected on a star path, and the pixel parallax is that a parallax value corresponding to the minimum aggregation cost is selected for each pixel on the stereo corrected satellite image as the pixel parallax, so as to obtain a parallax map.
And S52, optimizing the parallax map to obtain an optimized parallax map.
Optionally, the optimized disparity map can improve the disparity accuracy. The optimized disparity map may be regarded as a stereo Matching result of the stereo corrected satellite image pair, and the stereo Matching process of the stereo corrected satellite image pair may be implemented by an improved SGM (Semi-Global Matching) algorithm, which is not described in detail herein.
Optionally, the implementation method for obtaining the digital earth surface model and the three-dimensional point cloud model with the geographic position and the elevation information based on the optimized disparity map and the corrected RPC parameters includes: triangulating the optimized disparity map based on the corrected RPC parameters to obtain the digital surface model and the three-dimensional point cloud model
Alternatively, the digital earth model of site 1 is shown in FIG. 9 with geographic location coordinates (34 deg. 29'30.91 "S, 58 deg. 35' 15.86" W) at the intersection of the cross hairs, the location elevation value being 16.967625 meters. The three-dimensional point cloud model of the location 1 is shown in fig. 10, fig. 10 shows the three-dimensional point cloud model of 9 different view angles, the S2P method, the reconstruction method based on the conventional binding adjustment optimization algorithm, and the median error of the locations 1-5 in the satellite image processing method according to the embodiment is as follows
Table 1 shows:
TABLE 1
The protection scope of the satellite image processing method according to the embodiment of the present application is not limited to the execution sequence of the steps listed in the embodiment, and all the schemes implemented by adding or removing steps and replacing steps according to the prior art according to the principles of the present application are included in the protection scope of the present application.
As shown in fig. 11, the present embodiment provides a satellite image processing apparatus 1100, the satellite image processing apparatus 1100 including:
an image pair acquisition module 1110 for acquiring a satellite image pair and its associated initial RPC parameters.
The camera parameter obtaining module 1120 is configured to perform a binding adjustment optimization process on the matching point pair in the satellite image pair based on the initial RPC parameter, so as to obtain an euler angle required to be optimized for the camera associated with the satellite image pair, an optimized translation vector, and an optimized 3D point of the matching point pair, where an objective function value in the binding adjustment optimization process is determined by a reprojection error value and a translation correction value in a view angle of the camera associated with the satellite image pair, and the translation correction value is composed of a translation correction factor and the euler angle to be optimized.
A correction parameter obtaining module 1130, configured to obtain corrected RPC parameters based on the optimized euler angle, the optimized translation vector, the matching point pair, and the optimized 3D point.
In the satellite image processing apparatus 1100 provided in this embodiment, the image pair acquisition module 1110 corresponds to step S11 of the satellite image processing method shown in fig. 2, the camera parameter acquisition module 1120 corresponds to step S12 of the satellite image processing method shown in fig. 2, and the correction parameter acquisition module 1130 corresponds to step S13 of the satellite image processing method shown in fig. 2.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus or method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules/units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or units may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules or units, which may be in electrical, mechanical or other forms.
The modules/units illustrated as separate components may or may not be physically separate, and components shown as modules/units may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules/units may be selected according to actual needs to achieve the objectives of the embodiments of the present application. For example, functional modules/units in various embodiments of the application may be integrated into one processing module, or each module/unit may exist alone physically, or two or more modules/units may be integrated into one module/unit.
Those of ordinary skill would further appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment provides an electronic device, which comprises a memory, wherein a computer program is stored in the memory; and the processor is in communication connection with the memory and executes the satellite image processing method shown in fig. 2 when the computer program is called.
The embodiment of the application also provides a computer readable storage medium. Those of ordinary skill in the art will appreciate that all or part of the steps in a method implementing the above embodiments may be implemented by a program to instruct a processor, where the program may be stored in a computer readable storage medium, where the storage medium is a non-transitory (non-transitory) medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state disk, a magnetic tape (MAGNETIC TAPE), a floppy disk (floppy disk), a compact disk (optical disk), and any combination thereof. The storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Drive (SSD)), or the like.
Embodiments of the present application may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the processes or functions in accordance with embodiments of the present application are fully or partially developed. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.).
The computer program product is executed by a computer, which performs the method according to the preceding method embodiment. The computer program product may be a software installation package, which may be downloaded and executed on a computer in case the aforementioned method is required.
The descriptions of the processes or structures corresponding to the drawings have emphasis, and the descriptions of other processes or structures may be referred to for the parts of a certain process or structure that are not described in detail.
The above embodiments are merely illustrative of the principles of the present application and its effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that all equivalent modifications and variations of the application be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
1. A satellite image processing method, the satellite image processing method comprising:
Acquiring a satellite image pair and an associated initial RPC parameter thereof;
Based on the initial RPC parameters, carrying out binding adjustment optimization processing on the matching point pairs in the satellite image pair to obtain an Euler angle required to be optimized for a camera associated with the satellite image pair, an optimized translation vector and an optimized 3D point of the matching point pair, wherein an objective function value in the binding adjustment optimization processing is determined by a reprojection error value and a translation correction value under a view angle of the camera associated with the satellite image pair, and the translation correction value consists of a translation correction factor and the Euler angle to be optimized;
And acquiring corrected RPC parameters based on the optimized Euler angle, the optimized translation vector, the matching point pair and the optimized 3D point.
2. The satellite image processing method according to claim 1, wherein the implementation method for performing binding adjustment optimization processing on the matching point pairs in the satellite image pair to obtain the euler angles, the optimized translation vectors, and the optimized 3D points in the matching point pairs required to be optimized for the camera associated with the satellite image pair based on the initial RPC parameters includes:
performing feature extraction and matching processing on the satellite image pair to obtain a matching point pair in the satellite image pair;
triangulating the matching point pairs based on the initial RPC parameters to obtain initial 3D points of the matching point pairs;
Based on the initial 3D point, the matching point pair, a rotation matrix to be optimized, a translation vector to be optimized, the translation correction value and the objective function under a camera view angle related to the satellite image pair, the optimized euler angle, the optimized translation vector and the optimized 3D point are obtained, wherein the optimized euler angle, the optimized translation vector and the optimized 3D point are euler angles, translation vectors and 3D points related to the objective function value which is better under the camera view angle.
3. The satellite image processing method according to claim 2, wherein the rotation matrix at the mth camera view angle is expressed as:
Rm＝Rx(αm)Ry(βm)Rz(γm)
Wherein, R m represents a rotation matrix under the view angle of the mth camera, R x (DEG) represents rotation on the x axis, R y (DEG) represents rotation on the y axis, R z (DEG) represents rotation on the z axis, alpha m represents a first angle, beta m represents a second angle, gamma m represents a third angle, alpha m、βm and gamma m form an Euler angle to be optimized of the mth camera, and m is a positive integer not less than 1.
4. The satellite image processing method according to claim 1, further comprising:
performing stereo correction processing on the satellite image pair based on the corrected RPC parameters to obtain the stereo corrected satellite image pair;
Performing stereo matching processing on the corrected satellite image pair to obtain a parallax image of the corrected satellite image pair after optimization;
And acquiring a digital earth surface model and a three-dimensional point cloud model with geographic position and elevation information based on the optimized parallax map and the corrected RPC parameters.
5. The satellite image processing method according to claim 4, wherein the implementation method of performing a stereoscopic correction process on the satellite image pair based on the corrected RPC parameters to obtain the stereoscopic corrected satellite image pair includes:
Acquiring geographic coordinates and elevations of an area in a first satellite image based on a satellite positioning model associated with the first satellite image, wherein the first satellite image is one image in the satellite image pair, and the satellite positioning model is related to a corrected RPC parameter associated with the first satellite image;
Acquiring pseudo-matching points of the first satellite image based on the geographic coordinates, the elevation and a satellite projection model associated with a second satellite image, wherein the second satellite image is the other image in the satellite image pair, and the satellite projection model associated with the second satellite image is related to the corrected RPC parameter associated with the second satellite image;
Acquiring an image transformation matrix based on the pseudo-matching point pairs, the geographic coordinates and the elevation;
And performing image transformation processing on the second satellite image based on the image transformation matrix to acquire the stereoscopic corrected satellite image pair.
6. The satellite image processing method according to claim 4, wherein the implementation method for performing stereo matching processing on the corrected satellite image pair to obtain the corrected satellite image pair-optimized disparity map includes:
Acquiring a parallax map of the stereoscopic corrected satellite image pair based on the matching cost and the star path of the stereoscopic corrected satellite image pair, wherein the pixel parallax in the parallax map is a parallax value associated with the minimum aggregation cost of the pixels;
and carrying out optimization processing on the parallax map to obtain an optimized parallax map.
7. The satellite image processing method according to claim 6, wherein the obtaining a digital earth surface model and a three-dimensional point cloud model with geographical location and elevation information based on the optimized disparity map and the corrected RPC parameters includes: and carrying out triangulation on the optimized parallax map based on the corrected RPC parameters so as to acquire the digital surface model and the three-dimensional point cloud model.
8. A satellite image processing apparatus, characterized in that the satellite image processing apparatus comprises:
The image pair acquisition module is used for acquiring a satellite image pair and an associated initial RPC parameter;
the camera parameter acquisition module is used for carrying out binding adjustment optimization processing on the matching point pairs in the satellite image pair based on the initial RPC parameters so as to acquire an Euler angle required to be optimized for a camera associated with the satellite image pair, an optimized translation vector and an optimized 3D point of the matching point pair, wherein an objective function value in the binding adjustment optimization processing is determined by a reprojection error value and a translation correction value under a view angle of the camera associated with the satellite image pair, and the translation correction value consists of a translation correction factor and the Euler angle to be optimized;
and the correction parameter acquisition module is used for acquiring corrected RPC parameters based on the optimized Euler angle, the optimized translation vector, the matching point pair and the optimized 3D point.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the satellite image processing method according to any one of claims 1-7.
10. An electronic device, the electronic device comprising:
a memory storing a computer program;
a processor in communication with the memory, which when invoked performs the satellite image processing method of any one of claims 1-7.
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