An optical remote sensing satellite imaging simulation method and device

Abstract

The present application provides an optical remote sensing satellite imaging simulation method and device, relating to the technical field of optical imaging simulation. The method includes: obtaining the three-dimensional coordinates of the corresponding spatial points of each pixel of the simulation image of the optical remote sensing satellite; taking the intersection point of the line connecting the satellite camera optical center and the corresponding spatial point of each pixel with the Earth's surface as the projection point of the pixel, and calculating the longitude and latitude of the projection point; using the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel; correcting the texture color vector of each pixel using the solar visibility of the projection point; using the global atmospheric cloud image map and the longitude and latitude of the projection point to calculate the cloud image color vector of each pixel; and performing weighted fusion on the corrected texture color vector and the cloud image color vector of each pixel to obtain the rendering color vector of each pixel. The present application improves the simulation accuracy of the on-orbit imaging of the optical remote sensing satellite.

Description

Technical Field

[0001] The present application relates to the technical field of optical imaging simulation, and in particular, to an optical remote sensing satellite imaging simulation method and device. Background Art

[0002] The common method for optical remote sensing satellite imaging simulation is to establish an optical model and an error model of imaging, and through the physical mathematics of the imaging scene, simulate imaging by calculating the spot sensing function or based on the optical tracing method. Common methods include the modulation transfer function simulation method, the ray tracing method, etc.

[0003] The modulation transfer function method is based on the mutual conversion relationship between the modulation transfer function MTF, the line spread function LSF, and the point spread function PSF. Calculate its two-dimensional PSF matrix through the MTF of the optical system, and obtain the simulation image by performing two-dimensional PSF matrix degradation processing on the image. This method generates the simulation image by calculation according to the optical radiation distribution characteristics of the simulation scene, and must be carried out under the condition that the physical property distribution of the ground scene is known or simulated.

[0004] The ray tracing method is based on the ray tracing optical system modeling, models and calculates the imaging rays of the physical scene, then introduces the camera distortion modeling and simulation, and obtains the distorted image on the image plane after the distortion model degradation.

[0005] The simulation imaging of the prior art is based on the optical imaging model and needs to work under the condition of simulating the physical characteristics of the ground scene, lacking the flexible simulation ability for on-orbit remote sensing satellite imaging. There is a lack of simulation ability for jointly simulating clouds, real-time illumination shadows and other information. Summary of the Invention

[0006] In view of this, the present application provides an optical remote sensing satellite imaging simulation method and device to solve the above technical problems.

[0007] In a first aspect, an embodiment of the present application provides an optical remote sensing satellite imaging simulation method, including:

[0008] Obtain the three-dimensional coordinates of the corresponding spatial points of each pixel of the simulation image of the optical remote sensing satellite;

[0009] Take the intersection of the line connecting the optical center of the satellite camera and the corresponding spatial point of each pixel with the Earth's surface as the projection point of the pixel, and calculate the longitude and latitude of the projection point;

[0010] Use the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel;

[0011] Using the solar visibility of the projection point, determine whether the projection point is in the shadow, and correct the texture color vector of each pixel according to the determination result;

[0012] Using the global atmospheric cloud image map and the longitude and latitude of the projection point, calculate the cloud image color vector of each pixel;

[0013] Perform weighted fusion on the corrected texture color vector and the cloud image color vector of each pixel to obtain the rendering color vector of each pixel.

[0014] Furthermore, obtain the three-dimensional coordinates of the corresponding spatial points of each pixel of the simulation image of the optical remote sensing satellite; including:

[0015] For the pixel (i, j) of the simulation image, the coordinates in the image plane coordinate system are (u i,j , v i,j ), and the coordinates in the camera coordinate system are:

[0016]

[0017] where (i, j) represents the pixel coordinates, (u0, v0) are the coordinates of the center of the image plane, and f is the focal length;

[0018] Calculate the homogeneous coordinates of the spatial point corresponding to the pixel (i, j) in the Earth body coordinate system

[0019]

[0020] where M T-C is the transformation matrix from the Earth rectangular coordinate system to the camera coordinate system.

[0021] Furthermore, the transformation matrix M T-C from the Earth rectangular coordinate system to the camera coordinate system is:

[0022] M T-C = M T-S M S-C

[0023] where M T-S is the transformation matrix from the Earth rectangular coordinate system to the satellite body coordinate system; M S-C is the transformation matrix from the satellite body to the camera coordinate system.

[0024] Furthermore, the global texture image map is a distribution function with the longitude and latitude of the Earth's surface as independent variables and the texture color vector as the function value; using the global texture image map and the longitude and latitude of the projection point, obtain the texture color vector of each pixel; including:

[0025] The projection point corresponding to the pixel (i, j) of the simulation image is Pi,j , the longitude and latitude coordinates are (l on-(i,j) , l at-(i,j) ); find its four neighboring points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 on the global texture image map, and the longitude and latitude coordinates are (l on-(i,j)-1 , l at-(i,j)-1 ), (l on-(i,j)-1 , l at-(i,j)+1 ), (l on-(i,j)+1 , l at-(i,j)-1 ) and (l on-(i,j)+1 , l at-(i,j)+1 );

[0026] Obtain the texture color vectors of these four points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 from the texture map in sequence: (R i-1,j-1 , G i-1,j-1 , B i-1,j-1 ), (R i-1,j+1 , G i-1,j+1 , B i-1,j+1 ), (R i+1,j-1 , G i+1,j-1 , B i+1,j-1 ) and (R i+1,j+1 , G i+1,j+1 , B i+1,j+1 ); The three components of each texture color vector are: red value, green value and blue value;

[0027] Calculate the proportionality coefficients k lon and k lat :

[0028]

[0029]

[0030] Calculate the following intermediate values:

[0031] R′ i,j-1 = R i-1,j-1 + k lon (R i+1,j-1 - R i-1,j-1 )

[0032] R′ i,j+1 = R i-1,j+1 + k lon (R i+1,j-1 - R i-1,j+1 )

[0033] G′i,j-1 = G i-1,j-1 + k lon (G i+1,j-1 - G i-1,j-1 )

[0034] G' i,j+1 = G i-1,j+1 + k lon (G i+1,j-1 - G i-1,j+1 )

[0035] B' i,j-1 = B i-1,j-1 + k lon (B i+1,j-1 - B i-1,j-1 )

[0036] B' i,j+1 = B i-1,j+1 + k lon (B i+1,j-1 - B i-1,j+1 )

[0037] From this, the texture color vector of the intersection point is obtained: (R(u i,j , v i,j ), G(u i,j , v i,j ), B(u i,j , v i,j )):

[0038] R(u i,j , v i,j ) = R' i,j-1 + k lat (R' i,j+1 - R' i,j-1 )

[0039] G(u i,j , v i,j ) = G' i,j-1 + k lat (G' i,j+1 - G' i,j-1 )

[0040] B(u i,j , v i,j ) = B' i,j-1 + k lat (B' i,j+1 - B' i,j-1 )

[0041] Among them, R(u i,j , v i,j ) is the red value, G(u i,j , v i,j ) is the green value, B(ui,j , v i,j ) is the blue value.

[0042] Furthermore, using the solar visibility of the projection point, determine whether the projection point is a shadow, and correct the texture color vector of each pixel according to the judgment result; including:

[0043] Using the global digital topographic map, obtain the projection point P i,j at the terrain height Z L i,j corresponding point P L ;

[0044] Obtain the solar azimuth angle a i,j and the solar altitude angle e i,j ;

[0045] On the global digital topographic map, with point P L as the starting point, its geodetic coordinates are: (l on-(i,j) , l at-(i,j) , Z L i,j ), in the direction of the azimuth angle a i,j , search for the maximum elevation point P max within the maximum distance D L max , its geodetic coordinates are: (l on-(i,j)-max , l at-(i,j)-max , Z L (i,j)-max );

[0046] Calculate the altitude angle e T i,j :

[0047]

[0048] where D is the horizontal distance between the starting point P L and the maximum elevation point P L max ;

[0049] Judge whether e T i,j > e i,j holds. If so, then R(u i,j , v i,j ) = G(u i,j , v i,j ) = B(u i,j , v i,j ) = 0.

[0050] Further, the global atmospheric cloud image is a distribution function with the longitude and latitude of the Earth's surface as independent variables and the cloud image color vector as the function value; using the global atmospheric cloud image and the longitude and latitude of the projection point, calculate the cloud image color vector of each pixel; including:

[0051] Based on the projection point P i,j Find its four neighboring points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 on the global atmospheric cloud image;

[0052] Based on the longitude and latitude and cloud image color vectors of these four points, use bilinear interpolation to calculate the cloud image color vector of pixel (i,j): (R C (u i,j , v i,j ), G C (u i,j , v i,j ), B C (u i,j , v i,j ))), and the three components of the cloud image color vector are: red value, green value, and blue value.

[0053] Further, perform weighted fusion on the corrected color of each pixel and the cloud image color vector to obtain the rendered color vector of each pixel; including:

[0054] The three color values R sim (u i,j , v i,j ), G sim (u i,j , v i,j ) and B sim (u i,j , v i,j ) of pixel (i,j) in the simulation image are:

[0055] R sim (u i,j , v i,j ) = R(u i,j , v i,j )α c + R C (u i,j , v i,j )(1 - α c )

[0056] G sim (u i,j , v i,j ) = G(u i,j , v i,j )α c + GC (u i,j , v i,j )(1 - α c )

[0057] B sim (u i,j , v i,j ) = B(u i,j , v i,j )α c + B C (u i,j , v i,j )(1 - α c )

[0058] Where α c is the weight; then the rendered color vector is:

[0059] (R sim (u i,j , v i,j ), G sim (u i,j , v i,j ), B sim (u i,j , v i,j ))

[0060] In a second aspect, an optical remote sensing satellite imaging simulation device provided by an embodiment of the present application includes:

[0061] A position calculation unit, configured to obtain the three - dimensional coordinates of the corresponding spatial point of each pixel of the simulation image of the optical remote sensing satellite;

[0062] A ground intersection calculation unit, configured to use the connection line between the satellite camera optical center and the corresponding spatial point of each pixel as an intersection point on the earth's surface as the projection point of the pixel, and calculate the longitude and latitude of the projection point;

[0063] A color value calculation unit, configured to use the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel;

[0064] A color value correction unit, configured to use the solar visibility of the projection point to determine whether the projection point is in shadow, and correct the texture color vector of each pixel according to the determination result;

[0065] A cloud map color value calculation unit, configured to use the global atmospheric cloud image map and the longitude and latitude of the projection point to calculate the cloud map color vector of each pixel;

[0066] A fusion unit, configured to perform weighted fusion on the corrected texture color vector and the cloud map color vector of each pixel to obtain the rendered color vector of each pixel.

[0067] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the optical remote sensing satellite imaging simulation method of the embodiment of the present application is implemented.

[0068] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the optical remote sensing satellite imaging simulation method of the embodiment of the present application is implemented.

[0069] The present application improves the simulation accuracy of on-orbit imaging of optical remote sensing satellites. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0071] Figure 1 It is a flowchart of the optical remote sensing satellite imaging simulation method provided by the embodiment of the present application;

[0072] Figure 2 It is a functional structure diagram of the optical remote sensing satellite imaging simulation device provided by the embodiment of the present application;

[0073] Figure 3 It is a structure diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations.

[0075] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0076] First, a brief introduction to the design concept of the embodiments of the present application will be given.

[0077] The simulation imaging of the prior art is based on an optical imaging model and needs to work under the condition of simulating the physical characteristics of the ground scene, lacking the flexible simulation ability for the imaging of on-orbit remote sensing satellites. There is a lack of simulation ability for jointly simulating cloud layers, real-time lighting and shadows, etc.

[0078] To solve the above technical problems, the present application provides an optical remote sensing satellite imaging simulation method. Based on the joint application of a global topographic map, a texture image map, and an atmospheric cloud image map, and based on the satellite's orbit and geometric imaging model, it can flexibly and comprehensively simulate optical remote sensing imaging effects such as terrain occlusion and cloud effects, improving the authenticity of the simulation.

[0079] After introducing the application scenario and design concept of the embodiments of the present application, the technical solutions provided by the embodiments of the present application will be described below.

[0080] As Figure 1 shown, the embodiments of the present application provide an optical remote sensing satellite imaging simulation method, including:

[0081] Step 101: Obtain the three-dimensional coordinates of the corresponding spatial points of each pixel of the simulation image of the optical remote sensing satellite;

[0082] For the pixel (i, j) of the simulation image, its coordinates in the image plane coordinate system are (u i,j , v i,j ), and its coordinates in the camera coordinate system are:

[0083]

[0084] where (i, j) represents the pixel coordinates, (u0, v0) are the coordinates of the center of the image plane, and f is the focal length;

[0085] Using the installation parameters of the camera on the satellite body provided by the satellite generation department, the transformation matrix from the satellite body coordinate system to the camera imaging coordinate system can be known, and thus the position and attitude parameters of the camera imaging coordinate system and the Earth rectangular coordinate system can be calculated.

[0086] According to the orbital elements, calculate the position P T (X T , Y T , Z T ) of the optical remote sensing satellite in the Earth rectangular coordinate system O C_T =[x C_T y C_T z C_T , and according to the mission design of the remote sensing satellite, obtain the attitude information of the satellite body in the Earth rectangular space at the current moment to obtain the attitude transfer matrix

[0087] Calculate the translation matrix of the origin of the Earth's rectangular coordinate system in the satellite body coordinate system at this moment

[0088] Then the conversion relationship of the spatial point from the Earth's rectangular coordinate system to the satellite body coordinate system is X S = R T-S X T + T T-S ; Calculated using the homogeneous coordinate matrix, expressed as the following formula.

[0089]

[0090] Where is the homogeneous coordinate of the spatial point in the satellite body coordinate system, is the homogeneous coordinate of this point in the Earth's body coordinate system, M T-S is the transfer matrix from the Earth's rectangular coordinate system to the satellite body coordinate system:

[0091]

[0092] Calculate the homogeneous coordinate of the spatial point corresponding to the pixel (i, j) in the Earth's body coordinate system

[0093]

[0094] Where, M T-C is the conversion matrix from the Earth's rectangular coordinate system to the camera coordinate system.

[0095] The conversion matrix M from the Earth's rectangular coordinate system to the camera coordinate system T-C is:

[0096] M T-C = M T-S M S-C

[0097] Where, M T-S is the conversion matrix from the Earth's rectangular coordinate system to the satellite body coordinate system; M S-C is the conversion matrix from the satellite body to the camera coordinate system.

[0098] Step 102: Take the intersection of the line connecting the satellite camera optical center and the corresponding spatial point of each pixel with the Earth's surface as the projection point of the pixel, and calculate the longitude and latitude of the projection point;

[0099] Use the Earth's surface equation and the imaging ray equation to form a system of equations, as follows:

[0100]

[0101] where R E is the radius of the Earth; M R is a matrix;

[0102] According to the above derivation, for the pixel (i, j) of the simulation image, based on u i,j , v i,j values, use the above system of equations to solve and calculate its corresponding three-dimensional point in the geocentric rectangular coordinate system. This equation has two sets of solutions. Select the point with a smaller distance from point P C_T = [x C_T y C_T z C_T as the solution for the three-dimensional position of the imaging physical point, denoted as (x T-(i,j) , y T-(i,j) , z T-(i,j) ). Since this point is on the Earth's surface, therefore, its longitude and latitude coordinates (l on-(i,j) , l at-(i,j) ) can be calculated, where l on-(i,j) and l at-(i,j) are the longitude and latitude respectively.

[0103] Step 103: Use the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel;

[0104] In this embodiment, this step includes:

[0105] The projection point corresponding to the pixel (i, j) of the simulation image is P i,j , and its longitude and latitude coordinates are (l on-(i,j) , l at-(i,j) ); find its four neighboring points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 on the global texture image map, and their longitude and latitude coordinates are (l on-(i,j)-1 , l at-(i,j)-1 ), (l on-(i,j)-1 , l at-(i,j)+1 ), (l on-(i,j)+1 , l at-(i,j)-1 ) and (l on-(i,j)+1 , l at-(i,j)+1 ) respectively;

[0106] Successively obtain the texture color vectors of these four points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 from the texture map: (R i-1,j-1 , G i-1,j-1 , B i-1,j-1 ), (R i-1,j+1 , G i-1,j+1 , Bi-1,j+1 ), (R i+1,j-1 , G i+1,j-1 , B i+1,j-1 ) and (R i+1,j+1 , G i+1,j+1 , B i+1,j+1 ); Each of the three components of each texture color vector is: red value, green value, and blue value;

[0107] Calculate the proportionality coefficient k lon and k lat :

[0108]

[0109]

[0110] Calculate the following intermediate values:

[0111] R′ i,j-1 = R i-1,j-1 + k lon (R i+1,j-1 - R i-1,j-1 )

[0112] R′ i,j+1 = R i-1,j+1 + k lon (R i+1,j-1 - R i-1,j+1 )

[0113] G′ i,j-1 = G i-1,j-1 + k lon (G i+1,j-1 - G i-1,j-1 )

[0114] G′ i,j+1 = G i-1,j+1 + k lon (G i+1,j-1 - G i-1,j+1 )

[0115] B′ i,j-1 = B i-1,j-1 + k lon (B i+1,j-1 - B i-1,j-1 )

[0116] B′ i,j+1 = B i-1,j+1 + k lon (B i+1,j-1 - B i-1,j+1 )

[0117] Thus, the texture color vector of the intersection point is obtained: (R(u i,j , v i,j ), G(ui,j , v i,j ), B(u i,j , v i,j )):

[0118] R(u i,j , v i,j ) = R′ i,j-1 + k lat (R′ i,j+1 - R′ i,j-1 )

[0119] G(u i,j , v i,j ) = G′ i,j-1 + k lat (G′ i,j+1 - G′ i,j-1 )

[0120] B(u i,j , v i,j ) = B′ i,j-1 + k lat (B′ i,j+1 - B′ i,j-1 )

[0121] Wherein, R(u i,j , v i,j ) is the red value, G(u i,j , v i,j ) is the green value, and B(u i,j , v i,j ) is the blue value.

[0122] Step 104: Determine whether the projection point is a shadow by using the solar visibility of the projection point, and correct the texture color vector of each pixel according to the judgment result;

[0123] In this embodiment, this step includes:

[0124] Obtain the projection point P i,j at the terrain height Z L i,j ; L

[0125] Obtain the solar azimuth angle a i,j and the solar altitude angle e i,j ;

[0126] On the global digital topographic map, with the point P L as the starting point, whose geodetic coordinates are: (l on-(i,j) , l at-(i,j) , Z L i,j ), at the azimuth angle ai,j In the [direction], search for the maximum distance D max for the highest elevation point P within it L max , whose geodetic coordinates are: (l on-(i,j)-max , l at-(i,j)-max , Z L (i,j)-max );

[0127] Calculate the altitude angle e T i,j :

[0128]

[0129] where D is the horizontal distance between the starting point P L and the highest elevation point P L max ;

[0130] Judge whether e T i,j > e i,j holds. If so, then R(u i,j , v i,j ) = G(u i,j , v i,j ) = B(u i,j , v i,j ) = 0.

[0131] Step 105: Using the global atmospheric cloud image map and the longitude and latitude of the projection point, calculate the cloud map color vector of each pixel;

[0132] Based on the projection point P i,j find its four adjacent points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 on the global atmospheric cloud image map;

[0133] Based on the longitude and latitude of these four points and the cloud map color vector, use bilinear interpolation to calculate the cloud map color vector of the pixel (i, j): (R C (u i,j , v i,j ), G C (u i,j , v i,j ), B C (u i,j , v i,j ))). The three components of the cloud map color vector are: red value, green value, and blue value.

[0134] Step 106: Perform weighted fusion on the corrected texture color vector and the cloud map color vector of each pixel to obtain the rendering color vector of each pixel.

[0135] Specifically, the three color values R sim (u i,j , v i,j ), G sim (u i,j , v i,j ) and B sim (u i,j , v i,j ) of the pixel (i, j) in the simulation image are as follows:

[0136] R sim (u i,j , v i,j ) = R(u i,j , v i,j )α c + R C (u i,j , v i,j )(1 - α c )

[0137] G sim (u i,j , v i,j ) = G(u i,j , v i,j )α c + G C (u i,j , v i,j )(1 - α c )

[0138] B sim (u i,j , v i,j ) = B(u i,j , v i,j )α c + B C (u i,j , v i,j (1 - α c )

[0139] Among them, α c is the weight, and its value is 0.8;

[0140] Then the rendering color vector is:

[0141] (R sim (u i,j , v i,j ), G sim (u i,j , v i,j ), Bsim (u i,j , v i,j ))。

[0142] Based on the above embodiments, an optical remote sensing satellite imaging simulation device is provided in an embodiment of the present application. Referring to Figure 2 as shown, the optical remote sensing satellite imaging simulation device 200 provided in the embodiment of the present application at least includes:

[0143] A position calculation unit 201, configured to obtain the three-dimensional coordinates of the corresponding spatial point of each pixel of the simulation image of the optical remote sensing satellite;

[0144] A ground intersection calculation unit 202, configured to use the connection line between the optical center of the satellite camera and the corresponding spatial point of each pixel as an intersection point on the earth's surface as the projection point of the pixel, and calculate the longitude and latitude of the projection point;

[0145] A texture color calculation unit 203, configured to use the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel;

[0146] A texture color correction unit 204, configured to use the solar visibility of the projection point to determine whether the projection point is in shadow, and correct the texture color vector of each pixel according to the judgment result;

[0147] A cloud map color calculation unit 205, configured to use the global atmospheric cloud layer image map and the longitude and latitude of the projection point to calculate the cloud map color vector of each pixel;

[0148] A fusion unit 206, configured to perform weighted fusion on the corrected texture color vector and the cloud map color vector of each pixel to obtain the rendering color vector of each pixel.

[0149] It should be noted that the principle of the optical remote sensing satellite imaging simulation device 200 provided in the embodiment of the present application to solve technical problems is similar to that of the optical remote sensing satellite imaging simulation method provided in the embodiment of the present application. Therefore, for the implementation of the optical remote sensing satellite imaging simulation device 200 provided in the embodiment of the present application, reference can be made to the implementation of the optical remote sensing satellite imaging simulation method provided in the embodiment of the present application, and the repeated parts will not be elaborated.

[0150] As Figure 3 shown, the electronic device 300 provided in the embodiment of the present application at least includes: a processor 301, a memory 302, and a computer program stored on the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the optical remote sensing satellite imaging simulation method provided in the embodiment of the present application is implemented.

[0151] The electronic device 300 provided by the embodiments of the present application may further include a bus 303 that connects different components (including the processor 301 and the memory 302). Among them, the bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, etc.

[0152] The memory 302 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 3021 and / or a cache memory 3022, and may further include a read-only memory (ROM) 3023.

[0153] The memory 302 may further include a program tool 3024 having a set (at least one) of program modules 3025. The program modules 3025 include, but are not limited to: an operating subsystem, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0154] The electronic device 300 may also communicate with one or more external devices 304 (such as a keyboard, a remote control, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 300 (such as a mobile phone, a computer, etc.), and / or communicate with any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (such as a router, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 305. And, the electronic device 300 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 306. As Figure 3 shown, the network adapter 306 communicates with other modules of the electronic device 300 through the bus 303. It should be understood that although Figure 3 not shown in the figure, other hardware and / or software modules may be used in combination with the electronic device 300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, redundant arrays of independent disks (RAID) subsystems, tape drives, and data backup storage subsystems, etc.

[0155] It should be noted that Figure 3 the electronic device 300 shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0156] An embodiment of the present application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the optical remote sensing satellite imaging simulation method provided by the embodiment of the present application.

[0157] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the shown operations must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step and executed, and / or one step may be decomposed into multiple steps and executed.

[0158] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features. However, such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An optical remote sensing satellite imaging simulation method, characterized in that, Including: Obtaining the three-dimensional coordinates of the corresponding spatial points of each pixel in the simulation image of the optical remote sensing satellite; Taking the intersection point of the line connecting the satellite camera optical center and the corresponding spatial point of each pixel with the Earth's surface as the projection point of the pixel, and calculating the longitude and latitude of the projection point; Using the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel; wherein, the global texture image map is a distribution function with the longitude and latitude of the Earth's surface as independent variables and the texture color vector as the function value; Using the solar visibility of the projection point to determine whether the projection point is in shadow, and correcting the texture color vector of each pixel according to the judgment result; Using the global atmospheric cloud image map and the longitude and latitude of the projection point to calculate the cloud image color vector of each pixel; wherein, the global atmospheric cloud image map is a distribution function with the longitude and latitude of the Earth's surface as independent variables and the cloud image color vector as the function value; Performing weighted fusion on the corrected texture color vector and the cloud image color vector of each pixel to obtain the rendered color vector of each pixel.

2. The optical remote sensing satellite imaging simulation method according to claim 1, wherein Obtaining the three-dimensional coordinates of the corresponding spatial points of each pixel in the simulation image of the optical remote sensing satellite; including: For the pixel (i, j) of the simulation image, its coordinates in the image plane coordinate system are (u i,j , v i,j ), and its coordinates in the camera coordinate system are: Wherein, (i, j) represents the pixel coordinates, (u0, v0) is the coordinate of the center of the image plane, and f is the focal length; Calculate the homogeneous coordinates of the spatial point corresponding to the pixel (i, j) in the Earth's body coordinate system Among them, M T-C is the transformation matrix from the Earth rectangular coordinate system to the camera coordinate system.

3. The optical remote sensing satellite imaging simulation method according to claim 2, wherein The transformation matrix M from the Earth rectangular coordinate system to the camera coordinate system T-C is as follows: M T-C = M T-S M S-C Among them, M T-S is the transformation matrix from the Earth rectangular coordinate system to the satellite body coordinate system; M S-C is the transformation matrix from the satellite body to the camera coordinate system.

4. The optical remote sensing satellite imaging simulation method according to claim 2, characterized in that Using the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel; including: The projection point corresponding to the pixel (i, j) of the simulation image is P i,j , and the longitude and latitude coordinates are (l on-(i,j) , l at-(i,j) ); find its four adjacent points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 on the global texture image. The longitude and latitude coordinates are (l on-(i,j)-1 , l at-(i,j)-1 ), (l on-(i,j)-1 , l at-(i,j)+1 ), (l on-(i,j)+1 , l at-(i,j)-1 ) and (l on-(i,j)+1 , l at-(i,j)+1 ); Obtain these four points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 and P i+1,j+1 from the global texture image map in sequence, and their texture color vectors: (R i-1,j-1 , G i-1,j-1 , B i-1,j-1 ), (R i-1,j+1 , G i-1,j+1 , B i-1,j+1 ), (R i+1,j-1 , G i+1,j-1 , B i+1,j-1 ), and (R i+1,j+1 , G i+1,j+1 , B i+1,j+1 ); The three components of each texture color vector are: red value, green value, and blue value; Calculate the proportionality coefficient k lon and k lat : Calculating the following intermediate value: R′ i,j-1 = R i-1,j-1 + k lon (R i+1,j-1 - R i-1,j-1 ) R′ i,j+1 = R i-1,j+1 + k lon (R i+1,j-1 - R i-1,j+1 ) G′ i,j-1 = G i-1,j-1 + k lon (G i+1,j-1 - G i-1,j-1 ) G′ i,j+1 = G i-1,j+1 + k lon (G i+1,j-1 - G i-1,j+1 ) B′ i,j-1 = B i-1,j-1 + k lon (B i+1,j-1 - B i-1,j-1 ) B′ i,j+1 = B i-1,j+1 + k lon (B i+1,j-1 - B i-1,j+1 ) Thus, the texture color vector of the intersection point is obtained: (R(u i,j , v i,j ), G(u i,j , v i,j ), B(u i,j , v i,j )): R(u i,j ,v i,j ) = R′ i,j-1 +k lat (R′ i,j+1 -R′ i,j-1 ) G(u i,j ,v i,j ) = G′ i,j-1 +k lat (G′ i,j+1 -G′ i,j-1 ) B(u i,j ,v i,j ) = B′ i,j-1 +k lat (B′ i,j+1 -B′ i,j-1 ) where R(u i,j , v i,j ) is the red value, G(u i,j , v i,j ) is the green value, and B(u i,j , v i,j ) is the blue value.

5. The optical remote sensing satellite imaging simulation method according to claim 4, wherein Using the solar visibility of the projection point to determine whether the projection point is in shadow, and correcting the texture color vector of each pixel according to the judgment result; including: Using the global digital topographic map, obtain the projection point P i,j At the terrain height Z L i,j The corresponding point P L ; Obtain the solar azimuth angle a i,j and the solar altitude angle e i,j ; Starting from point P on the global digital topographic map L , its geodetic coordinates are: (l on-(i,j) , l at-(i,j) , Z L i,j ). Search for the highest elevation point P i,j within the maximum distance D max in the direction of azimuth a. Its geodetic coordinates are: (l L max , l on-(i,j)-max , l at-(i,j)-max , Z L (i,j)-max ); Calculate the elevation angle e T i,j : where D is the horizontal distance between the starting point P L and the highest elevation point P L max ; Determine e T i,j > e i,j Whether it holds. If so, then R(u i,j , v i,j ) = G(u i,j , v i,j ) = B(u i,j , v i,j ) = 0.

6. The optical remote sensing satellite imaging simulation method according to claim 5, characterized in that, Using the global atmospheric cloud image map and the longitude and latitude of the projection point to calculate the cloud image color vector of each pixel; including: Based on the projection point P i,j Find its four neighboring points P i-1,j-1 , P i-1,j+1 , P i+1,j-1 , and P i+1,j+1 ; Based on the longitude and latitude of these four points and the color vector of the cloud image, the color vector of the pixel (i, j) is calculated using bilinear interpolation: (R C (u i,j , v i,j ), G C (u i,j , v i,j ), B C (u i,j , v i,j ))). The three components of the cloud image color vector are: red value, green value, and blue value.

7. The optical remote sensing satellite imaging simulation method according to claim 6, characterized in that Performing weighted fusion on the corrected color and the cloud image color vector of each pixel to obtain the rendered color vector of each pixel; including: The three color values R of the pixel (i, j) in the simulation image sim (u i,j , v i,j ), G sim (u i,j , v i,j ), and B sim (u i,j , v i,j ): R sim (u i,j ,v i,j ) = R(u i,j ,v i,j )α c +R C (u i,j ,v i,j )(1 - α c ) G sim (u i,j ,v i,j ) = G(u i,j ,v i,j )α c + G C (u i,j ,v i,j )(1 - α c ) B sim (u i,j ,v i,j ) = B(u i,j ,v i,j )α c + B C (u i,j ,v i,j )(1 - α c ) where α c is the weight; then the rendering color vector is: (R sim (u i,j , v i,j ), G sim (u i,j , v i,j ), B sim (u i,j , v i,j ))。 8. An optical remote sensing satellite imaging simulation device, characterized in that, Including: A position calculation unit for obtaining the three-dimensional coordinates of the corresponding spatial points of each pixel in the simulation image of the optical remote sensing satellite; A ground intersection calculation unit for taking the intersection point of the line connecting the satellite camera optical center and the corresponding spatial point of each pixel with the Earth's surface as the projection point of the pixel and calculating the longitude and latitude of the projection point; A color value calculation unit for using the global texture image map and the longitude and latitude of the projection point to obtain the texture color vector of each pixel; wherein, the global texture image map is a distribution function with the longitude and latitude of the Earth's surface as independent variables and the texture color vector as the function value; A color value correction unit for using the solar visibility of the projection point to determine whether the projection point is in shadow and correcting the texture color vector of each pixel according to the judgment result; A cloud image color value calculation unit for using the global atmospheric cloud image map and the longitude and latitude of the projection point to calculate the cloud image color vector of each pixel; wherein, the global atmospheric cloud image map is a distribution function with the longitude and latitude of the Earth's surface as independent variables and the cloud image color vector as the function value; A fusion unit for performing weighted fusion on the corrected texture color vector and the cloud image color vector of each pixel to obtain the rendered color vector of each pixel.

9. An electronic device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the optical remote sensing satellite imaging simulation method according to any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the optical remote sensing satellite imaging simulation method according to any one of claims 1-7 is implemented.

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