CN112050735B - Method for recursion refinement of ground position of optical remote sensing satellite big data and storage medium - Google Patents
Method for recursion refinement of ground position of optical remote sensing satellite big data and storage medium Download PDFInfo
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Abstract
The invention provides a method for recursion refinement of ground position of optical remote sensing satellite big data and a storage medium, comprising the steps of calculating a first co-factor and initial three-dimensional position data of a ground point according to a first piece of optical remote sensing satellite data and a second piece of optical remote sensing satellite data; calculating and updating the co-factor according to the newly added optical remote sensing satellite data and the first co-factor; calculating a gain value according to the newly added optical remote sensing satellite data and the co-factor; calculating and updating the three-dimensional position data of the ground points according to the initial three-dimensional position data of the ground points and the gain values in a recursion manner; repeatedly executing the newly added optical remote sensing satellite and then recursively calculating and updating the three-dimensional position data of the ground point; the first optical remote sensing satellite data, the second optical remote sensing satellite data and the newly added optical remote sensing satellite data comprise rotation matrix data; the method can calculate in real time, improves the calculation efficiency, saves the calculation resources, cancels the initial approximate values and the iteration in the traditional calculation, and has accurate and reliable updating results.
Description
Technical Field
The disclosure relates to the technical field of remote sensing image processing, in particular to a method for recursion refinement of ground position of optical remote sensing satellite big data and a storage medium.
Background
With the development of remote sensing technology, the number of optical remote sensing satellites in orbit operation is greatly increased, and the satellite remote sensing has the characteristic of big data. Therefore, the obtained optical remote sensing data volume is larger and larger, and the types of the data are richer. The ground position refinement processing of the satellite remote sensing big data is the core and key for improving the application of the remote sensing big data, the ground position precision can be improved according to the refinement processing, but the position refinement and the precision mining of the optical remote sensing satellite big data of the existing satellite remote sensing big data system are obviously insufficient.
The accuracy of calculating the ground position by using the optical remote sensing satellite data generally depends on the high-accuracy satellite attitude and orbit measurement and the design of a remote sensor system, so that the usability of improving the ground position accuracy of the optical remote sensing data is greatly limited. And the stereopair formed based on two optical remote sensing satellite data can calculate the point three-dimensional position of the ground position, but the precision is also limited. The big data of the optical remote sensing satellite can observe a plurality of optical remote sensing satellites in the same position area on the ground in a multi-angle, multi-orbit, multi-height and multi-resolution mode, and the satellite remote sensing data has inherent geometric consistency, so that the space geometric relation of the big data of the optical remote sensing satellite can be fully applied, and the precision of the ground position is refined. And with the increase of the number of the optical remote sensing satellites, the precision of the ground position can be gradually improved, namely the three-dimensional position of the ground point can be refined by utilizing the big data of the optical remote sensing satellites.
The conventional least square integral adjustment method can be adopted for refining the ground position by utilizing the optical remote sensing satellite big data, but when the adjustment method is applied, once new optical remote sensing satellite data is added, adjustment calculation needs to be carried out from the beginning, namely, all the existing optical remote sensing satellite data and the newly added optical remote sensing satellite data need to be integrated and processed once again, so that in order to obtain a position refining result of new data addition, the conventional adjustment method needs to be started from the beginning, and all processing flows are repeated. With the continuous increase of optical satellite remote sensing big data, the complexity of operation is rapidly increased, and the integral adjustment method is not suitable for real-time measurement and large-scale engineering application.
Disclosure of Invention
Technical problem to be solved
In view of the fact that the three-dimensional position of the ground point is updated by using a traditional least square integral adjustment method according to data of a plurality of remote sensing satellites at present, the calculation method is long, the efficiency is low, and the calculation process for the number of satellites is complex.
(II) technical scheme
The embodiment of the disclosure provides a method for recursion refinement of ground position of optical remote sensing satellite big data, which comprises the following steps:
and calculating a first covariance factor and initial three-dimensional position data of the ground point according to the first optical remote sensing satellite data and the second optical remote sensing satellite data.
And calculating and updating the co-factor according to the newly added optical remote sensing satellite data and the first co-factor.
And calculating a gain value according to the newly added optical remote sensing satellite data and the first co-factor.
And recursively calculating and updating the three-dimensional position data of the ground points according to the initial three-dimensional position data of the ground points and the gain values.
And repeating the execution of the newly added optical remote sensing satellite data and then recursively calculating and updating the three-dimensional position data of the ground point.
The first optical remote sensing satellite data, the second optical remote sensing satellite data and the newly added optical remote sensing satellite data comprise rotation matrix data.
According to some embodiments of the disclosure, the rotation matrix of the first optical remote sensing satellite is calculated by attitude angle data of the first optical remote sensing satellite, and the attitude angle of the first optical remote sensing satellite comprises a pitch angle alpha1Side roll angle beta1And yaw angle gamma1And 9 elements of the rotation matrix of the first optical remote sensing satellite comprise:
a11＝cosα1cosγ1-sinα1sinβ1sinγ1
a12＝-cosα1sinγ1-sinα1sinβ1cosγ1
a13＝-sinα1cosβ1
b11＝cosβ1sinγ1
b12＝cosβ1cosγ1
b13＝-sinβ1
c11＝sinα1cosγ1+cosα1sinβ1sinγ1
c12＝-sinα1sinγ1+cosα1sinβ1cosγ1
c13＝cosα1cosβ1；
calculating a rotation matrix of the second optical remote sensing satellite according to attitude angle data of the second optical remote sensing satellite data, wherein the attitude angle of the second optical remote sensing satellite data comprises a pitch angle alpha2Side roll angle beta2And yaw angle gamma2And 9 elements of the rotation matrix of the second piece of optical remote sensing satellite data comprise:
a21＝cosα2cosγ2-sinα2sinβ2sinγ2
a22＝-cosα2sinγ2-sinα2sinβ2cosγ2
a23＝-sinα2cosβ2
b21＝cosβ2sinγ2
b22＝cosβ2cosγ2
b23＝-sinβ2
c21＝sinα2cosγ2+cosα2sinβ2sinγ2
c22＝-sinα2sinγ2+cosα2sinβ2cosγ2
c23＝cosα2cosβ2。
according to some embodiments of the disclosure, the calculating the first covariance factor from the first optical remote sensing satellite data and the second optical remote sensing satellite data comprises the sub-steps of:
extracting the two-dimensional image coordinates (x) of the ground points on the first optical remote sensing satellite data1，y1) The two-dimensional image coordinate (x) of the ground point on the second optical remote sensing satellite data2，y2)；
Calculating a first co-factor, wherein the first co-factor is calculated according to a formula:
wherein Q0Representing the first-time co-factor of the calculation, is a 3-row 3-column matrix, A0A matrix of 4 rows and 3 columns of coefficient entries,
is a matrix A0Transpose of (c) ()-1Representing the inverse calculation of the matrix;
coefficient term matrix A0The calculation formula (2) includes:
wherein f is01The focal length f of the optical camera corresponding to the first optical remote sensing satellite data02And the focal length of the optical camera corresponding to the second optical remote sensing satellite data.
According to some embodiments of the disclosure, the calculating initial three-dimensional position data for the ground points comprises: calculating initial three-dimensional position data of the ground points according to the data of the first optical remote sensing satellite, the data of the second optical remote sensing satellite and the first covariance factor, wherein a calculation formula of the initial three-dimensional position data of the ground points comprises:
wherein L is0A matrix of constant terms of 4 rows and 1 column, X0The initial three-dimensional position data of the ground points obtained by calculation is a matrix with 3 rows and 1 column, and represents three values of the initial three-dimensional position data of the ground points.
According to some embodiments of the disclosure, a satellite three-dimensional position (X) corresponding to the first optical remote sensing satellite data is extractedS1，YS1，ZS1) Satellite three-dimensional position (X) corresponding to said second optical remote sensing satellite dataS2，YS2，ZS2)；
The constant term matrix L0The calculation formula (2) includes:
according to some embodiments of the present disclosure, the iteratively calculating and updating the three-dimensional position data of the ground point after repeatedly executing the newly added optical remote sensing satellite data includes:
newly adding kth optical remote sensing satellite data, wherein a calculation formula of a kth gain value is as follows:
wherein G iskIs the k gain value, Qk-1The (k-1) th co-factor, A, calculated for the last recursionkIs a matrix of coefficient terms in 2 rows and 3 columns,
is AkTranspose of (c) ()-1The inverse calculation of the matrix is shown, E is a unit matrix with 3 rows and 3 columns, k is a positive integer, and the times of updating the three-dimensional position data of the ground points through recursive calculation are shown;
coefficient term matrix AkThe calculation formula of (a) is as follows:
wherein (a)(k+2)1，a(k+2)2，a(k+2)3，b(k+2)1，b(k+2)2，b(k+2)3，c(k+2)1，c(k+2)2，c(k+2)3) As an element of the newly added rotation matrix of the kth optical remote sensing satellite, fkFor the newly added optical camera focal length corresponding to the kth optical remote sensing satellite data, (x)k+2，yk+2) And the coordinates of the two-dimensional image of the ground point on the newly added kth optical remote sensing satellite data are obtained.
According to some embodiments of the present disclosure, the iteratively calculating and updating the three-dimensional position data of the ground point after repeatedly executing the newly added optical remote sensing satellite data includes:
newly adding kth optical remote sensing satellite data, wherein the calculation formula of the kth co-factor is as follows:
Qk＝Qk-1-GkAkQk-1
wherein Q iskIs a k-th co-factor, Qk-1For the (k-1) th co-factor, G, calculated for the last recursionkIs the k gain value.
According to some embodiments of the present disclosure, the iteratively calculating and updating the three-dimensional position data of the ground point after repeatedly executing the newly added optical remote sensing satellite data includes:
newly adding kth optical remote sensing satellite data, and updating the three-dimensional position data of the ground point by the kth recursion calculation, wherein the calculation formula is as follows:
Xk＝Xk-1+GkLk
wherein, XkFor the three-dimensional position, X, of the ground point obtained by the k-th updatek-1Three-dimensional position, L, of said ground point for the last updatekA matrix of constant terms of 2 rows and 1 column.
According to some embodiments of the disclosure, the matrix of constant terms LkThe calculation formula of (a) is as follows:
(XS(k+2)，YS(k+2)，ZS(k+2)) And the data is satellite three-dimensional position data corresponding to the kth optical remote sensing satellite data.
Embodiments of the present disclosure also provide a storage medium storing a program for implementing the method.
(III) advantageous effects
The embodiment of the present disclosure can calculate the three-dimensional position of the ground point by the data of the two remote sensing satellites, and update the three-dimensional position of the ground point according to the known three-dimensional position of the ground point, the data of the newly added remote sensing satellite and the co-factor, which has the following advantages,
(1) and successively recursion updating is carried out according to the sequence of the satellite remote sensing data to update the three-dimensional position of the ground point, incremental calculation is carried out only on newly added optical satellite data during recursion updating, and the previous remote sensing satellite data does not need to participate in calculation again, so that the calculation efficiency is improved, and the calculation resources are saved.
(2) The calculation of each recursion update is simple, the matrix scale is within three orders, and the real-time calculation is facilitated.
(3) The refinement updating directly and explicitly calculates the three-dimensional position of the ground point without initial approximate values and iteration and approximation processes in the calculation.
Drawings
Fig. 1 is a flowchart of a method for recursive refinement of ground position of optical remote sensing satellite big data according to an embodiment of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Furthermore, in the following description, descriptions of well-known technologies are omitted so as to avoid unnecessarily obscuring the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises" and "comprising," when used herein, specify the presence of stated features, steps, or operations, but do not preclude the presence or addition of one or more other features, steps, or operations.
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
In an exemplary embodiment of the present disclosure, a method for recursively refining ground position of optical remote sensing satellite big data is provided, and fig. 1 is a flowchart of a method for recursively refining ground position of optical remote sensing satellite big data according to an embodiment of the present disclosure, as shown in fig. 1, the method for recursively refining ground position of optical remote sensing satellite big data according to an embodiment of the present disclosure includes operations S1 to S5.
In operation S1, a first covariance factor and initial three-dimensional position data of a ground point are calculated from the first optical remote sensing satellite data and the second optical remote sensing satellite data.
In operation S2, an update co-factor is calculated based on the newly added optical remote sensing satellite data and the first co-factor.
In operation S3, a gain value is calculated based on the newly added optical remote sensing satellite data and the first covariance factor.
In operation S4, three-dimensional position data of the updated ground points are recursively calculated based on the initial three-dimensional position data of the ground points and the gain values.
In operation S5, operations S2 through S4 are repeatedly performed to successively recurrently calculate three-dimensional position data of updated ground points.
The first optical remote sensing satellite data, the second optical remote sensing satellite data and the newly added optical remote sensing satellite data comprise rotation matrix data.
The following describes in detail each step of the method for recursive refinement of ground position of optical remote sensing satellite big data in this embodiment.
According to some embodiments of the disclosure, the first piece of optical remote sensing satellite data comprises a rotation matrix calculated from the first piece of optical remote sensing satellite attitude angle data.
The attitude angle is defined according to the euler concept and is also called euler angle. The attitude angle is determined by the relation between the target coordinate system and the geographic coordinate system and is expressed by three Euler angles of yaw angle, pitch angle and roll angle.
And obtaining a coordinate transformation matrix between the body coordinate system where the target is located and the geographic coordinate system through the target attitude angle.
Data from a first optical remote sensing satelliteExtracting attitude angle information of the optical remote sensing satellite, wherein the attitude angle of the first optical remote sensing satellite comprises a pitch angle alpha1Side roll angle beta1And yaw angle gamma1Calculating a rotation matrix of the first optical remote sensing satellite according to the data, wherein 9 elements of the rotation matrix of the first optical remote sensing satellite comprise
a11＝cosα1cosγ1-sinα1sinβ1sinγ1
a12＝-cosα1sinγ1-sinα1sinβ1cosγ1
a13＝-sinα1cosβ1
b11＝cosβ1sinγ1
b12＝cosβ1cosγ1
b13＝-sinβ1
c11＝sinα1cosγ1+cosα1sinβ1sinγ1
c12＝-sinα1sinγ1+cosα1sinβ1cosγ1
c13＝cosα1cosβ1。
The second optical remote sensing satellite data comprises a rotation matrix calculated according to attitude angle data corresponding to the second optical remote sensing satellite data.
Extracting attitude angle information of the optical remote sensing satellite from data of a second optical remote sensing satellite, wherein the attitude angle of the second optical remote sensing satellite comprises a pitch angle alpha2Side roll angle beta2And yaw angle gamma2Calculating a rotation matrix of a second optical remote sensing satellite by the data, wherein 9 elements of the rotation matrix of the second optical remote sensing satellite comprise
a21＝cosα2cosγ2-sinα2sinβ2sinγ2
a22＝-cosα2sinγ2-sinα2sinβ2cosγ2
a23＝-sinα2cosβ2
b21＝cosβ2sinγ2
b22＝cosβ2cosγ2
b23＝-sinβ2
c21＝sinα2cosγ2+cosα2sinβ2sinγ2
c22＝-sinα2sinγ2+cosα2sinβ2cosγ2
c23＝cosα2cosβ2。
According to some embodiments of the disclosure, the first optical remote sensing satellite data and the second optical remote sensing satellite data further comprise extracting two-dimensional image coordinates (x) of the ground point on the first optical remote sensing satellite data1，y1) Extracting two-dimensional image coordinates (x) of ground points on second optical remote sensing satellite data2，y2)。
Extracting the data from the first optical remote sensing satellite data and the second remote sensing satellite data and calculating an initial co-factor, wherein a calculation formula of the initial co-factor comprises
Wherein A is0A matrix of 4 rows and 3 columns of coefficient entries,
is a matrix A0Transpose of (c) ()-1Representing the inverse calculation of the matrix, A0The calculation formula of (a) includes,
wherein Q is0For initial co-factor, a 3-row and 3-column matrix, f01Focal length f of optical camera corresponding to first optical remote sensing satellite data02The focal length of the optical camera corresponding to the second optical remote sensing satellite data is f01And f02Can be obtained by the corresponding satellite camera developer, a11、a12、a13、b11、b12、b13、c11、c12、c13And a21、a22、a23、b21、b22、b23、c21、c22、c23And elements of the rotation matrix calculated for the attitude angles of the first optical remote sensing satellite and the second optical remote sensing satellite.
According to some embodiments of the present disclosure, the calculating of the initial three-dimensional position data of the ground points is embodied by calculating the initial three-dimensional position data of the ground points from the data of the first and second optical remote sensing satellites and the initial co-factor, and the calculation formula of the initial three-dimensional position data of the ground points comprises
Wherein L is0A matrix of constant terms of 4 rows and 1 column, X0The initial three-dimensional position data of the ground points obtained by calculation is a matrix with 3 rows and 1 column, and represents three values of the initial three-dimensional position data of the ground points.
According to some embodiments of the disclosure, a satellite three-dimensional position (X) corresponding to the first optical remote sensing satellite data is extractedS1，YS1，ZS1) Satellite three-dimensional position (X) corresponding to second optical remote sensing satellite dataS2，YS2，ZS2)。
Matrix of constant terms L0Is calculated by the formula including
According to some embodiments of the present disclosure, recursively calculating three-dimensional position data of updated ground points after repeatedly executing newly added optical remote sensing satellite data includes:
newly adding kth optical remote sensing satellite data, wherein a calculation formula of a kth gain value is as follows:
wherein G iskIs the k gain value, Qk-1The (k-1) th co-factor, A, calculated for the last recursionkIs a matrix of coefficient terms in 2 rows and 3 columns,
is AkTranspose of (c) ()-1The method is characterized in that the matrix is subjected to inversion calculation, E is a unit matrix with 3 rows and 3 columns, k is a positive integer, and the times of updating the three-dimensional position data of the ground point through recursion calculation are represented.
Coefficient term matrix AkThe calculation formula of (a) is as follows:
wherein (a)(k+2)1，a(k+2)2，a(k+2)3，b(k+2)1，b(k+2)2，b(k+2)3，c(k+2)1，c(k+2)2，c(k+2)3) As an element of the newly added rotation matrix of the kth optical remote sensing satellite, fkFocal length of optical camera for the new kth optical remote sensing satellite, (x)k+2，yk+2) And (4) two-dimensional image coordinates of the ground point on the newly added kth optical remote sensing satellite data.
When k is 1, namely when a first optical remote sensing satellite is newly added, first recursion calculation is carried out to update the three-dimensional position data of the ground point,
according to some embodiments of the present disclosure, iteratively calculating after adding a new optical remote sensing satellite to update three-dimensional position data of ground points comprises:
newly adding kth optical remote sensing satellite data, wherein the calculation formula of the kth co-factor is as follows:
Qk＝Qk-1-GkAkQk-1
wherein Q iskIs a k-th co-factor, Qk-1For the (k-1) th co-factor, G, calculated for the last recursionkIs the k gain value.
According to some embodiments of the present disclosure, recursively calculating three-dimensional position data of updated ground points after repeatedly executing newly added optical remote sensing satellite data includes:
newly adding kth optical remote sensing satellite data, and recursively calculating and updating the three-position data of the ground point at the kth time, wherein the calculation formula is as follows:
Xk＝Xk-1+GkLk
wherein, XkFor the three-dimensional position, X, of the ground point obtained for the kth updatek-1For the three-dimensional position of the ground point obtained from the last update, LkA matrix of constant terms of 2 rows and 1 column.
According to some embodiments of the disclosure, the matrix of constant terms LkThe calculation formula of (a) is as follows:
(XS(k+2)，YS(k+2)，ZS(k+2)) And the satellite three-dimensional position data corresponding to the kth optical remote sensing satellite data.
The embodiment of the disclosure also provides a storage medium which stores a program for realizing the method for recurrently refining the ground position of the big data of the optical remote sensing satellite.
The method for recursion refinement of ground position of optical remote sensing satellite big data provided by the embodiment of the disclosure is not inherently related to any specific computer, virtual system or other equipment. Various general purpose systems may also be used with the teachings herein. Moreover, this disclosure is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the disclosure as described herein.
The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. Embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present disclosure may also be embodied as apparatus or device programs, e.g., computer programs and computer program products, for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
The above embodiments are provided to further explain the purpose, technical solutions and advantages of the present disclosure in detail, and it should be understood that the above embodiments are only examples of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (5)
1. A method for recursion refinement of ground position of optical remote sensing satellite big data is characterized by comprising the following steps:
calculating a rotation matrix of a first optical remote sensing satellite through attitude angle data of the first optical remote sensing satelliteAttitude angle of remote sensing satellite includes pitch angle alpha1Side roll angle beta1And yaw angle gamma1And 9 elements of the rotation matrix of the first optical remote sensing satellite comprise:
a11＝cosα1cosγ1-sinα1sinβ1sinγ1
a12＝-cosα1sinγ1-sinα1sinβ1cosγ1
a13＝-sinα1cosβ1
b11＝cosβ1sinγ1
b12＝cosβ1cosγ1
b13＝-sinβ1
c11＝sinα1cosγ1+cosα1sinβ1sinγ1
c12＝-sinα1sinγ1+cosα1sinβ1cosγ1
c13＝cosα1cosβ1；
calculating a rotation matrix of a second optical remote sensing satellite according to attitude angle data of the second optical remote sensing satellite, wherein the attitude angle of the second optical remote sensing satellite comprises a pitch angle alpha2Side roll angle beta2And yaw angle gamma2And 9 elements of the rotation matrix of the second optical remote sensing satellite comprise:
a21＝cosα2cosγ2-sinα2sinβ2sinγ2
a22＝-cosα2sinγ2-sinα2sinβ2cosγ2
a23＝-sinα2cosβ2
b21＝cosβ2sinγ2
b22＝cosβ2cosγ2
b23＝-sinβ2
c21＝sinα2cosγ2+cosα2sinβ2sinγ2
c22＝-sinα2sinγ2+cosα2sinβ2cosγ2
c23＝cosα2cosβ2
calculating a first-time co-factor and initial three-dimensional position data of a ground point according to the first optical remote sensing satellite data and the second optical remote sensing satellite data, wherein the calculation of the first-time co-factor comprises the following steps:
extracting the two-dimensional image coordinates (x) of the ground points on the first optical remote sensing satellite data1，y1) The two-dimensional image coordinate (x) of the ground point on the second optical remote sensing satellite data2，y2)；
Calculating a first co-factor, wherein the first co-factor is calculated according to a formula:
wherein Q0Representing the first-time co-factor of the calculation, is a 3-row 3-column matrix, A0A matrix of 4 rows and 3 columns of coefficient entries,
is a matrix A0Transpose of (c) ()-1Representing the inverse calculation of the matrix;
coefficient term matrix A0The calculation formula (2) includes:
wherein f is01The first optical remote sensing satellite data corresponds to the focal length f of an optical camera02For said second number of optically remote sensing satellitesAccording to the focal length of the corresponding optical camera;
the calculating initial three-dimensional position data of ground points comprises: calculating initial three-dimensional position data of the ground points according to the data of the first optical remote sensing satellite, the data of the second optical remote sensing satellite and the first covariance factor, wherein a calculation formula of the initial three-dimensional position data of the ground points comprises:
wherein L is0A matrix of constant terms of 4 rows and 1 column, X0For the initial three-dimensional position data of the ground points obtained by calculation, the three-dimensional position data is a matrix with 3 rows and 1 column, and represents three numerical values of the initial three-dimensional position data of the ground points;
extracting a satellite three-dimensional position (X) corresponding to the first optical remote sensing satellite dataS1，YS1，ZS1) And a satellite three-dimensional position (X) corresponding to said second optical remote sensing satellite dataS2，YS2，ZS2)；
The constant term matrix L0The calculation formula (2) includes:
calculating a gain value according to the newly added optical remote sensing satellite data and the first co-factor, wherein the formula for calculating the gain value is as follows:
wherein G is1As a gain value, A1Is a matrix of coefficient terms in 2 rows and 3 columns,
is A1Is transferred to，()-1The matrix is subjected to inversion calculation, and E is an identity matrix with 3 rows and 3 columns;
coefficient term matrix A1The calculation formula of (a) is as follows:
wherein (a)31，a32，a33，b31，b32，b33，c31，c32，c33) Element of rotation matrix corresponding to the newly added optical remote sensing satellite data, f1(x) the focal length of the optical camera corresponding to the newly added optical remote sensing satellite data3，y3) Two-dimensional image coordinates of the newly added optical remote sensing satellite data for the ground points;
calculating an updated co-factor according to the newly added optical remote sensing satellite data and the first co-factor;
the calculation formula of the update co-factor is as follows: q1＝Q0-G1A1Q0
Wherein Q iskTo update the co-factor, Q01Is a first-time synergy factor;
recursively calculating and updating the three-dimensional position data of the ground points according to the initial three-dimensional position data of the ground points and the gain values;
the calculation formula is as follows: x1＝X0+G1L1
Wherein, X1For updating the obtained three-dimensional position data of the ground points, X0Is the initial three-dimensional position data of the ground point, L1A matrix of constant terms in 2 rows and 1 column;
the constant term matrix L1The calculation formula of (a) is as follows:
wherein (X)S3，YS3，ZS3) The satellite three-dimensional position corresponding to the newly added optical remote sensing satellite data is obtained;
repeatedly executing newly-added optical remote sensing satellite data and then recursively calculating and updating the three-dimensional position data of the ground point;
the first optical remote sensing satellite data, the second optical remote sensing satellite data and the newly added optical remote sensing satellite data comprise rotation matrix data.
2. The method for recursive refinement of big data of optical remote sensing satellite according to claim 1, wherein said recursive calculation after repeatedly executing new data of optical remote sensing satellite updates three-dimensional position data of said ground point, comprising:
newly adding kth optical remote sensing satellite data, wherein a calculation formula of a kth gain value is as follows:
wherein G iskIs the k gain value, Qk-1The (k-1) th co-factor, A, calculated for the last recursionkIs a matrix of coefficient terms in 2 rows and 3 columns,
is AkTranspose of (c) ()-1The inverse calculation of the matrix is shown, E is a unit matrix with 3 rows and 3 columns, k is a positive integer, and the times of updating the three-dimensional position data of the ground points through recursive calculation are shown;
coefficient term matrix AkThe calculation formula of (a) is as follows:
wherein (a)(k+2)1，a(k+2)2，a(k+2)3，b(k+2)1，b(k+2)2，b(k+2)3，c(k+2)1，c(k+2)2，c(k+2)3) For the newly added element of the rotation matrix corresponding to the kth optical remote sensing satellite data, fkFor the newly added optical camera focal length corresponding to the kth optical remote sensing satellite data, (x)k+2，yk+2) And the coordinates of the newly added kth two-dimensional image of the optical remote sensing satellite data of the ground point are obtained.
3. The method for recursive refinement of big data of optical remote sensing satellite according to claim 2, wherein said recursive calculation after repeatedly executing new data of optical remote sensing satellite updates three-dimensional position data of said ground point, comprising:
newly adding kth optical remote sensing satellite data, wherein the calculation formula of the kth co-factor is as follows:
Qk＝Qk-1-GkAkQk-1
wherein Q iskIs a k-th co-factor, Qk-1For the (k-1) th co-factor, G, calculated for the last recursionkIs the k gain value.
4. The method for recursive refinement of big data of optical remote sensing satellite according to claim 3, wherein said recursive calculation after repeatedly executing new data of optical remote sensing satellite updates three-dimensional position data of said ground point, comprising:
newly adding kth optical remote sensing satellite data, and recursively calculating and updating the three-position data of the ground point at the kth time, wherein the calculation formula is as follows:
Xk＝Xk-1+GkLk
wherein, XkFor the three-dimensional position, X, of the ground point obtained by the k-th updatek-1Three-dimensional position, L, of said ground point for the last updatekA matrix of constant terms in 2 rows and 1 column;
the constant term matrix LkThe calculation formula of (a) is as follows:
(XS(k+2)，YS(k+2)，ZS(k+2)) And the data is corresponding three-dimensional position data in the kth optical remote sensing satellite data.
5. A storage medium characterized by storing a program for implementing the method according to any one of claims 1 to 4.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2847609A1 (en) * | 2012-05-03 | 2015-03-18 | Raytheon Company | Position and elevation acquisition for orbit determination |
| CN108230326A (en) * | 2018-02-08 | 2018-06-29 | 重庆市地理信息中心 | Satellite image garland based on GPU-CPU collaborations deforms rapid detection method |
| CN108761444A (en) * | 2018-05-24 | 2018-11-06 | 中国科学院电子学研究所 | The method that joint satellite-borne SAR and optical imagery calculate spot height |
| CN108981673A (en) * | 2018-05-24 | 2018-12-11 | 中国科学院电子学研究所 | The method for calculating ground point three-dimensional position using sequence star-loaded optical remote sensing image |
| CN110030978A (en) * | 2019-04-17 | 2019-07-19 | 武汉大学 | A kind of full link optical satellite geometry imaging model construction method and system |
| CN111766580A (en) * | 2020-07-10 | 2020-10-13 | 中国科学院空天信息创新研究院 | Method for successively recursion refinement of ground position of SAR remote sensing satellite big data |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9383210B2 (en) * | 2014-01-30 | 2016-07-05 | James L. Carr | Image navigation and registration (INR) transfer from exquisite systems to hosted space payloads |
| CN105182315A (en) * | 2015-07-14 | 2015-12-23 | 中国科学院电子学研究所 | Method for obtaining remote sensing image ground resolution of large swing angle optical remote sensing satellite |
-
2020
- 2020-09-09 CN CN202010944211.6A patent/CN112050735B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2847609A1 (en) * | 2012-05-03 | 2015-03-18 | Raytheon Company | Position and elevation acquisition for orbit determination |
| CN108230326A (en) * | 2018-02-08 | 2018-06-29 | 重庆市地理信息中心 | Satellite image garland based on GPU-CPU collaborations deforms rapid detection method |
| CN108761444A (en) * | 2018-05-24 | 2018-11-06 | 中国科学院电子学研究所 | The method that joint satellite-borne SAR and optical imagery calculate spot height |
| CN108981673A (en) * | 2018-05-24 | 2018-12-11 | 中国科学院电子学研究所 | The method for calculating ground point three-dimensional position using sequence star-loaded optical remote sensing image |
| CN110030978A (en) * | 2019-04-17 | 2019-07-19 | 武汉大学 | A kind of full link optical satellite geometry imaging model construction method and system |
| CN111766580A (en) * | 2020-07-10 | 2020-10-13 | 中国科学院空天信息创新研究院 | Method for successively recursion refinement of ground position of SAR remote sensing satellite big data |
Non-Patent Citations (1)
| Title |
|---|
| 多重观测卫星影像的无控区域网平差;姚星辉等;《遥感技术与应用》;20180630;第33卷(第3期);全文 * |
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