CN111766580A - Method for successively recursion refinement of ground position of SAR remote sensing satellite big data - Google Patents
Method for successively recursion refinement of ground position of SAR remote sensing satellite big data Download PDFInfo
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Abstract
A method for successively recursion refinement of ground position of SAR remote sensing satellite big data comprises the following steps: calculating a first synergistic factor and a ground position by utilizing data of two SAR remote sensing satellites; extracting the ground point image coordinates of newly added SAR satellite remote sensing data, and calculating the gain value of the newly added data; calculating a three-dimensional position newly added with the SAR satellite remote sensing data for updating; calculating an updated co-factor after newly adding SAR satellite remote sensing data; and repeatedly executing recursive calculation until all the remote sensing data are processed. The method carries out successive recursive refined calculation according to the sequence of the SAR satellite remote sensing data, only one newly added SAR satellite data needs to be subjected to incremental calculation during recursive calculation, and processing from the beginning is not needed.
Description
Technical Field
The invention relates to the technical field of remote sensing image processing, in particular to a method for successively recurrently refining ground positions of SAR remote sensing satellite big data.
Background
With the development of the synthetic aperture radar (SAR for short, hereinafter, the SAR is generally expressed by SAR) technology, the number of SAR remote sensing satellites which run in orbit at home and abroad is greatly increased, and SAR remote sensing has the characteristic of big data, so that the data volume of the obtained SAR remote sensing data is larger and larger, and the types of the SAR remote sensing data are richer. The accuracy of the three-dimensional position of the ground point can be improved by utilizing SAR remote sensing big data, but the existing SAR satellite remote sensing big data system is obviously insufficient in ground position refinement processing.
The ground position calculation of the single SAR remote sensing satellite data depends on high-precision satellite attitude and speed measurement and radar system design, and the three-dimensional position precision of a ground point calculated by three-dimensional positioning based on the two SAR remote sensing satellite data is limited. The SAR remote sensing satellite big data can be used for carrying out multi-angle, multi-orbit, multi-height and multi-resolution observation on the same ground area by a plurality of SAR satellites, and the data has internal geometric consistency, so that the precision of the ground position can be improved by fully applying repeated observation processing of the SAR remote sensing satellite big data, and the precision of the three-dimensional position can be gradually improved along with the increase of the number of the SAR remote sensing satellites, therefore, the three-dimensional position of the ground point can be refined by utilizing the SAR remote sensing satellite big data.
When the SAR remote sensing satellite big data is used for carrying out three-dimensional refinement calculation, a traditional least square integral adjustment method can be adopted, once new SAR remote sensing satellite data is added, the SAR remote sensing satellite data needs to be integrated from the beginning, namely, all the existing SAR remote sensing satellite data and the newly added SAR remote sensing satellite data need to be processed again, along with the continuous increase of the SAR remote sensing satellite big data, the complexity of calculation is rapidly increased, and the SAR remote sensing satellite big data is not suitable for real-time calculation and large-scale engineering application.
Disclosure of Invention
In view of the above, the present invention provides a method for successively recurrently refining ground position of big data of a SAR remote sensing satellite, so as to partially solve at least one of the above technical problems.
In order to achieve the above object, as an aspect of the present invention, there is provided a method for successively recurrently refining a ground position of SAR remote sensing satellite big data, comprising the steps of:
calculating a first synergistic factor and a ground position by utilizing data of two SAR remote sensing satellites;
extracting the ground point image coordinates of newly added SAR satellite remote sensing data, and calculating the gain value of the newly added data;
calculating a three-dimensional position newly added with the SAR satellite remote sensing data for updating;
calculating an updated co-factor after newly adding SAR satellite remote sensing data;
and repeatedly executing recursive calculation until all the remote sensing data are processed.
Based on the technical scheme, compared with the prior art, the method for successively recurrently refining the ground position of the SAR remote sensing satellite big data has at least one of the following beneficial effects:
(1) successive recursive refined calculation is carried out according to the sequence of the SAR satellite remote sensing data, incremental calculation is carried out only on newly added SAR satellite data during recursive calculation, and processing from the beginning is not needed.
(2) The calculation of the recursion refinement process is simple, the matrix scale is within three orders, and the real-time calculation is facilitated.
(3) The three-dimensional position of the ground point is explicitly calculated in the refinement calculation, an initial approximate value is not needed, and iteration and an approximate process are not needed in the calculation.
Drawings
FIG. 1 is a flow chart of a method for successively recurrently refining ground positions of big data of SAR remote sensing satellites according to an embodiment of the invention.
Detailed Description
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
As shown in fig. 1, a flowchart of a method for successively recurrently refining a ground position of SAR remote sensing satellite big data according to an embodiment of the present invention specifically includes the following steps:
step A: and performing first calculation of the co-factor and the ground position by using the data of the two SAR remote sensing satellites.
This step can be further divided into the following three substeps
Step A1: extracting two-dimensional image coordinates (x) of ground points on the two SAR remote sensing satellite data by using an image matching or artificial method1，y1)(x2，y2)：
Step A2: calculating a first co-factor, wherein the formula for calculating the co-factor is as follows:
wherein Qk-1Representing the first-time co-factor of the calculation, is a 3-row, 3-column matrix.
Ak-1A coefficient term matrix with 3 rows and 3 columns, wherein each element of the matrix is specifically expressed as follows:
(XS1，YS1，ZS1) And (X)S2，YS2，ZS2) The three-dimensional positions of the corresponding satellites on the two SAR remote sensing data respectively (V)X1，VY1，VZ1) And (V)X2，VY2，VZ2) The satellite three-dimensional speeds corresponding to the two SAR remote sensing data can be extracted from the auxiliary data of the SAR remote sensing satellite data and are corresponding to the row coordinate y of the ground point1，y2The values are obtained by interpolation processing.
And A isT k-1Representation pair matrix Ak-1A transposition calculation of the matrix is performed.
()-1Indicating that the matrix is subjected to an inversion calculation.
The subscript with k in the letter represents the recursion times of the refinement calculation, k is valued as 2 in the first calculation, and the value is increased by 1 every recursion time in the subsequent calculation.
Step A3: calculating the first three-dimensional position of the ground point, wherein the calculation formula is as follows:
wherein Xk-1For the calculated three-dimensional position of the ground, a matrix of 3 rows and 1 column is used, representing three values of the three-dimensional position.
Qk-1Representing the calculated co-factor, is the 3 row, 3 column matrix calculated by step a 2.
Lk-1A constant matrix with 3 rows and 1 column, wherein each element of the matrix is specifically expressed as follows:
wherein x is1And x2The coordinate values of the image column directions of the ground point on the two pieces of SAR remote sensing data are respectively obtained in step A1. (X)S1，YS1，ZS1) And (X)S1，YS1，ZS1) The three-dimensional positions of the corresponding satellites on the 1 st and 2 nd SAR remote sensing data respectively, and (V)X1，VY1，VZ1) And (V)X2，Vt2，VZ2) The satellite three-dimensional speeds corresponding to the 1 st SAR remote sensing data and the 2 nd SAR remote sensing data can be extracted from auxiliary data of the SAR remote sensing satellite data and are according to the row coordinate y corresponding to the ground point1，y2The values are obtained by interpolation processing. r is1And r2Perigee slope, M, for 1 st and 2 nd SAR dataX1And MX2Pitch resolution, λ, for 1 st and 2 nd SAR data1And λ2Radar wavelength, f, for 1 st and 2 nd SAR dataD1And fD2The four parameters are known parameters of satellite-borne SAR imaging, namely Doppler center frequencies of 1 st SAR data and 2 nd SAR data, and can be directly read from auxiliary data of SAR satellites.
And B: after newly adding an SAR satellite remote sensing data, extracting the image coordinates of the ground point, and calculating the gain value of the newly added data, wherein the calculation formula is as follows:
wherein G iskFor the calculated gain value of the newly added SAR remote sensing data,
Akcoefficient term matrix of 3 rows and 3 columns
Qk-1Representing the co-factor of the last recursion calculation,
e denotes an identity matrix of 3 rows and 3 columns,
()-1indicating that the matrix is subjected to an inversion calculation.
And A iskEach element of the matrix is specifically represented as follows:
(XS1，YS1，ZS1) And (X)Sk，YSk，ZSk) The three-dimensional positions of the corresponding satellites on the 1 st SAR remote sensing data and the newly added (k th) SAR remote sensing data respectively, and (V)X1，VY1，VZ1) And (V)Xk，VYk，VZk) The satellite three-dimensional speeds corresponding to the No. 1 SAR remote sensing data and the newly added (No. k) SAR remote sensing data can be extracted from the auxiliary data of the SAR remote sensing satellite data and are according to the row coordinate y corresponding to the ground point1，y2The values are obtained by interpolation processing.
And C: calculating the updated three-dimensional position after newly adding SAR satellite remote sensing data, wherein the calculation formula is as follows:
Xk＝Xk-1+GkLk
wherein XkFor the calculated three-dimensional position of the ground, Xk-1For the three-dimensional position, X, of the ground obtained from the last recursion calculationkAnd Xk-1Are all 3 rows and 1 column matrices representing three values for a three dimensional position.
GkAnd B, calculating a gain value of newly added remote sensing data according to the step B.
LkA constant term matrix with 3 rows and 1 column, wherein each element of the matrix is specifically expressed as follows:
wherein x is1The coordinate value of the ground point in the image column direction of the 1 st SAR data is obtained by the step A1, xkAdding the image column direction coordinate value of the (k th) SAR data for the ground point newly, and performing the step BExtracting to obtain the extract. (X)S1，YS1，ZS1) And (X)Sk，YSk，ZSk) The three-dimensional positions of the corresponding satellites on the SAR remote sensing data of the 1 st and newly added (k) frames respectively, and (V)X1，VY1，VZ1) And (V)Xk，VYk，VZk) The satellite three-dimensional speeds corresponding to the SAR remote sensing data of the 1 st and the newly added (k) frames can be extracted from the auxiliary data of the SAR remote sensing satellite data and are according to the row coordinate y corresponding to the ground point1，ykThe values are obtained by interpolation processing. r is1And rkShort-range slope, M, for 1 st and newly added (kth) SAR dataX1And MXkPitch resolution, λ, for 1 st and newly added (kth) SAR data1And λkRadar wavelength, f, for SAR data of 1 st and newly added (k-th) framesD1And fDkThe four parameters are known parameters of satellite-borne SAR imaging and can be directly read from auxiliary data of SAR satellites for the Doppler center frequency of the 1 st SAR data and the newly added (k th) SAR data.
Step D: calculating an updated co-factor after newly adding SAR satellite remote sensing data, wherein the calculation formula is as follows:
Qk＝Qk-1-GkAkQk-1
Qkrepresenting the calculated updated co-factor matrix, Qk-1Representing the co-factor matrix of the last recursion calculation.
GkAnd B, calculating a gain value of newly added remote sensing data according to the step B.
AkAnd B, a coefficient term matrix of 3 rows and 3 columns is also obtained by calculation in the step B.
Step E: the calculation of step B to step D is repeatedly performed. And for SAR satellite remote sensing big data, repeating the calculation from the step B to the step D in sequence after adding one piece of remote sensing data from the 3 rd data, and recursively finishing the refinement of the three-dimensional position of the ground point once the calculation from the step B to the step D is finished.
In conclusion, the successive recursion refinement ground position method of SAR remote sensing satellite big data of the invention directly and explicitly calculates the three-dimensional position of the ground point by applying the platform parameter and the radar parameter of the satellite-borne SAR remote sensing image without any initial value setting and iteration and approximation. And after every satellite remote sensing data is added, the current refined position of the new SAR remote sensing satellite data added can be obtained by recursion calculation only by utilizing the refined value and the co-factor of the refined value which are already obtained by the previous SAR remote sensing satellite data. It can be seen that the recursive calculation is simple, and the matrix scale is within three orders. For newly-added remote sensing data, processing from the beginning is not needed, and only the estimation result of the prior data is utilized and the contribution of the newly-added data is considered.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for successively recursion refinement of ground position of SAR remote sensing satellite big data is characterized by comprising the following steps:
calculating a first synergistic factor and a ground position by utilizing data of two SAR remote sensing satellites;
extracting the ground point image coordinates of newly added SAR satellite remote sensing data, and calculating the gain value of the newly added data;
calculating a three-dimensional position newly added with the SAR satellite remote sensing data for updating;
calculating an updated co-factor after newly adding SAR satellite remote sensing data;
and repeatedly executing recursive calculation until all the remote sensing data are processed.
2. The method according to claim 1, characterized in that said step of calculating the first-time co-factor and the ground position using two SAR remote sensing satellite data comprises the following sub-steps:
extracting two-dimensional image coordinates (x) of ground points on two SAR remote sensing satellite data by using image matching or artificial method1，y1)(x2，y2)；
Calculating a first co-factor, wherein the formula for calculating the co-factor is as follows:
wherein Q isk-1The first-time co-factor representing the calculation is a 3-row and 3-column matrix; a. thek-1A coefficient term matrix of 3 rows and 3 columns; a. theT k-1Representation pair matrix Ak-1Perform the transpose calculation of the matrix, ()-1Representing the inverse calculation of the matrix; the subscript with k in the letter represents the recursion times of the refinement calculation, k is valued as 2 in the first calculation, and the value is increased by 1 every recursion time in the subsequent calculation.
Calculating the first three-dimensional position of the ground point, wherein the calculation formula is as follows:
wherein, Xk-1The ground three-dimensional position obtained by calculation is a matrix with 3 rows and 1 column, and represents three numerical values of the three-dimensional position; l isk-1A constant matrix of 3 rows and 1 column.
3. The method of claim 2, wherein a isk-1Each element of the matrix is specifically represented as follows:
(XS1，YS1，ZS1) And (X)S2，YS2，ZS2) The three-dimensional positions of the corresponding satellites on the two SAR remote sensing data respectively (V)X1，VY1，VZ1) And (V)X2，VY2，VZ2) The satellite three-dimensional speeds corresponding to the two SAR remote sensing data can be extracted from the auxiliary data of the SAR remote sensing satellite data and are respectively corresponding to the row coordinate y of the ground point1，y2The values are obtained by interpolation processing.
4. The method of claim 2, wherein L isk-1Each element of the matrix is specifically represented as follows:
wherein x is1And x2The coordinate values of the image array directions of the ground points on the two SAR remote sensing data are respectively r1And r2Perigee slope, M, for 1 st and 2 nd SAR dataX1And MX2For the slant range resolution, λ, of the 1 st and 2 nd SAR data1And λ2Radar wavelength, f, for 1 st and 2 nd SAR dataD1And fD2The four parameters are known parameters of satellite-borne SAR imaging, namely Doppler center frequencies of 1 st SAR data and 2 nd SAR data, and can be directly read from auxiliary data of SAR satellites.
5. The method according to claim 1, wherein the formula for extracting the ground point image coordinates of the newly added SAR satellite remote sensing data and calculating the gain value of the newly added data is as follows:
wherein G iskFor calculated gain values of newly added SAR remote sensing data, AkA matrix of coefficient terms of 3 rows and 3 columns, Qk-1Represents the co-factor of the last recursion calculation, E represents the 3 rows and 3 columns identity matrix, ()-1Indicating that the matrix is subjected to an inversion calculation.
6. The method of claim 5, wherein the method is performed in a batch processIs characterized in that A iskEach element of the matrix is specifically represented as follows:
(XS1，YS1，ZS1) And (X)Sk，YSk，ZSk) The 1 st and newly added frames are respectively the corresponding satellite three-dimensional positions on the k frame SAR remote sensing data, and (V)X1，VY1，VZ1) And (V)Xk，VYk，VZk) The three-dimensional speeds of the satellites corresponding to the No. 1 SAR remote sensing data and the new SAR remote sensing data, namely the three-dimensional speeds of the satellites corresponding to the kth SAR remote sensing data, can be extracted from auxiliary data of the SAR remote sensing satellite data and are corresponding to the row-direction coordinate y of the ground point1，y2The values are obtained by interpolation processing.
7. The method according to claim 1, wherein in the step of calculating the updated three-dimensional position of the newly added SAR satellite remote sensing data, the calculation formula of the updated three-dimensional position is as follows:
Xk＝Xk-1+GkLk；
wherein, XkFor the calculated three-dimensional position of the ground, Xk-1For the three-dimensional position, X, of the ground obtained from the last recursion calculationkAnd Xk-1All are 3 rows and 1 column matrices, representing three values of three-dimensional position; l iskA matrix of constant terms in 3 rows and 1 column.
8. The method of claim 7, wherein L iskEach element of the matrix is specifically represented as follows:
wherein r is1And rkShort-range slope, M, for the 1 st and newly added frames, i.e., the kth SAR dataX1And MXkFor the 1 st and newly added frames, i.e. the slope resolution, lambda, of the k-th SAR data1And λkFor the 1 st and newly added frames, i.e. the radar wavelength, f, of the k-th SAR dataD1And fDkThe four parameters are known parameters of satellite-borne SAR imaging and can be directly read from auxiliary data of SAR satellites for the 1 st frame and a newly added frame, namely the k frame SAR data Doppler center frequency.
9. The method according to claim 1, wherein in the step of calculating the updated co-factor after adding new SAR satellite remote sensing data, the updated co-factor calculation formula is as follows:
Qk＝Qk-1-GkAkQk-1；
wherein Q iskRepresenting the calculated updated co-factor matrix, Qk-1Representing the co-factor matrix of the last recursion calculation.
10. The method according to claim 1, wherein in the step of repeatedly performing recursive calculation until all the remote sensing data are processed, the recursive calculation comprises extracting ground point image coordinates of newly added SAR satellite remote sensing data and calculating gain values of the newly added SAR satellite remote sensing data; calculating a three-dimensional position newly added with the SAR satellite remote sensing data for updating; and calculating an updated co-factor after newly adding the SAR satellite remote sensing data.
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