Interference image compression method and system for in-orbit processing of interference imaging altimeter

Abstract

The invention provides an interference image compression method and system for in-orbit processing of an interference imaging altimeter. The method comprises the following steps: reading interference image data and a pixel ground distance of the interference imaging altimeter; generating a uniformly changing ground distance grid according to the ground distance coverage range of the interference image; performing linear interpolation on the interference image according to the generated ground distance grid; performing weighted average on the processed interference image; and outputting the processed interference image. According to the method, the high-ratio interference image compression can be realized while the high measurement precision of the interference imaging altimeter is ensured, and the spatial frequency spectrum information of the interference image is improved by using window weighting. The FPGA system is realized on the basis of pipeline design, and the method has the advantages of low time delay, low resource consumption and easiness in weight window change, and can meet the actual application requirements of interference image compression in on-orbit processing of the interference imaging altimeter.

Description

Technical Field

[0001] The present application belongs to the field of interferometric imaging altimeters, and specifically relates to an interferometric image compression method and system for on-orbit processing of interferometric imaging altimeters. Background Art

[0002] Interferometric imaging altimeter is a new generation of satellite-borne radar altimeter, which can realize wide-swath, high-resolution and high-precision sea surface height measurement. Due to the full-time working system, the amount of raw data of interferometric imaging altimeter is huge. In order to alleviate the pressure of satellite-to-ground data transmission, it is necessary to significantly reduce the amount of data while maintaining the height measurement accuracy. Using general data compression methods to directly compress echo data will introduce large errors to the height measurement and cannot meet the height measurement accuracy requirements. Therefore, it is necessary to process the echo data on-orbit to obtain the interference image, and achieve high-magnification data compression through image compression methods. A commonly used image compression method in the field of synthetic aperture radar is to divide the image into blocks and sum and average, reducing the amount of data and improving the signal-to-noise ratio by sacrificing image resolution. However, the ground resolution of the interferometric imaging altimeter changes continuously along the distance direction, and the ground resolution of the near-distance point of the interferometric image is much lower than that of the far-distance point. The above-mentioned method of directly compressing the interferometric image in blocks will introduce large interference phase errors at the near-satellite point, which also cannot meet the height measurement accuracy requirements. In order to maintain the height measurement accuracy while compressing the interferometric image, the problem of the change of the ground resolution of the interferometric imaging altimeter must be solved. Therefore, in response to the on-orbit processing application requirements of interferometric imaging altimeters, a new interferometric image compression method and system are proposed to efficiently realize on-orbit compression of interferometric images, which is of great significance to the research of the next generation of interferometric imaging altimeters. Summary of the invention

[0003] The purpose of this application is to overcome the defects of interference image compression in the on-orbit processing of interferometric imaging altimeters, while reducing the impact of interference image compression on the accuracy of altitude measurement, improving the utilization efficiency of on-board hardware resources, and realizing on-orbit compression of interference images with low error, low latency, and low resource consumption.

[0004] In order to achieve the above-mentioned object, the present application proposes an interference image compression method for on-orbit processing of an interference imaging altimeter, the method comprising:

[0005] Step 1) reading in interference image data and pixel ground distance of an interference imaging altimeter;

[0006] Step 2) generating a uniformly varying ground distance grid according to the ground distance coverage of the interference image;

[0007] Step 3) performing linear interpolation on the interference image according to the generated ground distance grid;

[0008] Step 4) performing weighted averaging on the interference image after the linear interpolation processing;

[0009] Step 5) Output the interference image after weighted average processing.

[0010] As an improvement of the above method, the step 2) comprises:

[0011] Step 2-1) Calculate the number of points N that are evenly spaced from the grid rg :

[0012]

[0013] in, Indicates the rounding down operation; R g Indicates the ground distance coverage of the interference image; d rg Indicates evenly spaced distances;

[0014] Step 2-2) Generate a uniformly varying ground distance grid using the maximum ground distance as a reference:

[0015] r g (n) = r max -d rg ×(N rg -n),0≤n≤N rg -1

[0016] Among them, r g (n) represents the nth uniformly spaced grid point; r max Indicates the maximum ground distance of the interference image; N rg Indicates the number of points evenly spaced from the grid; d rg Indicates evenly spaced spaces.

[0017] As an improvement of the above method, in step 3), the interpolation position is calculated by sliding window comparison, that is, a comparison window sliding on the interference image is set, and the interpolation position is calculated by comparing the uniform ground distance grid with the pixel ground distance in the comparison window. The position of the comparison window changes continuously as the image interpolation is performed, so that the interpolation position of each uniform ground distance grid is correctly calculated, including the following steps:

[0018] Step 3-1) Read the pixel distance covered by the current comparison window;

[0019] Step 3-2) comparing the uniform distance grid and the pixel distance;

[0020] Step 3-3) decoding the comparison result and calculating the interpolation position of the current uniform distance grid;

[0021] Step 3-4) Read in the interference image pixels and pixel-to-pixel distances according to the calculated interpolation positions;

[0022] Step 3-5) completes the linear interpolation calculation;

[0023] Step 3-6) uses the current interpolation position as the position of the next comparison window and returns to step 3-1).

[0024] As an improvement of the above method, the linear interpolation calculation method is as follows:

[0025]

[0026] Among them, s g Represents the interpolated pixel; r g Represents a uniform ground distance grid; s0 represents the first pixel involved in the interpolation; r0 represents the ground distance of the first pixel; s1 represents the second pixel involved in the interpolation; r1 represents the ground distance of the second pixel.

[0027] As an improvement of the above method, the step 4) comprises:

[0028] Step 4-1) dividing the interference image after linear interpolation processing into pixel neighborhoods;

[0029] Step 4-2) Obtain the weighted window coefficient f required for weighted averaging of interference images m,n ;

[0030] Step 4-3) Use the obtained weighted window coefficient to perform weighted averaging on all divided pixel neighborhoods:

[0031]

[0032] Among them, x m,n represents the interference image pixel involved in the pixel area; f m,n represents the weighted window coefficient; y represents the new pixel after weighted averaging; M, N represent the number of points in the azimuth and distance directions of the pixel neighborhood.

[0033] As an improvement of the above method, the weighted window coefficient supports dynamic setting.

[0034] The present application also provides an interference image compression system for on-orbit processing of an interference imaging altimeter, which is implemented based on the above method, and the system includes:

[0035] A data cache module, used to read and cache the interference image data and pixel ground distance of the interference imaging altimeter;

[0036] A grid generation module, used for generating a uniform ground distance grid according to the ground distance coverage of the interference image;

[0037] An image interpolation module is used to perform a linear interpolation operation on the interference image according to the uniformly spaced grid generated by the grid generation module;

[0038] The weighted average module is used to perform weighted average calculation on the interference image processed by the image interpolation module and output the processing result.

[0039] As an improvement of the above system, the system is implemented on FPGA and adopts a full-process pipeline design.

[0040] Compared with the prior art, the advantages of this application are:

[0041] The present invention adopts a pipeline design, interpolates the interference image to a uniform ground-distance grid and then compresses it, and improves the spatial spectrum information of the interference image through window weighting. Based on the above method and system, the present invention can eliminate the influence of the rapid change of the ground-distance resolution of the interferometric imaging altimeter, ensure the height measurement accuracy of the interferometric image after on-orbit compression, and at the same time has the advantages of low latency, low resource consumption, and easy change of weighted windows, which can meet the practical application requirements of interference image compression in on-orbit processing of interferometric imaging altimeter. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Shown is a flow chart of an interferometric image compression method for on-orbit processing of an interferometric imaging altimeter;

[0043] Figure 2 The figure shows the interference phase diagram of the measured data of the interferometric imaging altimeter without the interference image compression processing;

[0044] Figure 3 The figure shows the interference phase diagram after the interference image compression processing is performed on the measured data of the interference imaging altimeter by the method of the present invention;

[0045] Figure 4 It shows the height measurement error curves introduced by the method of the present invention and the block compression method respectively on the central azimuth line of the interference image;

[0046] Figure 5 The figure shows the implementation block diagram of the interferometric image compression system for on-orbit processing of interferometric imaging altimeter;

[0047] Figure 6 Shown is a workflow diagram of the image interpolation module in the system of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0049] like Figure 1 As shown, the present invention proposes an interference image compression method for on-orbit processing of an interference imaging altimeter, comprising the following steps:

[0050] Step 1) reading in the interference image data and the ground distance of each pixel of the interference imaging altimeter;

[0051] Step 2) generating a uniformly varying ground distance grid according to the ground distance coverage of the interference image;

[0052] Step 2) further comprises:

[0053] Step 2-1) Calculate the number of points N that are evenly spaced from the grid rg :

[0054]

[0055] In the formula, Indicates the rounding down operation, R g represents the ground distance coverage of the interferometric image, d rg Indicates evenly spaced spaces.

[0056] Step 2-2) Generate a uniformly varying ground distance grid using the maximum ground distance as a reference:

[0057] r g (n) = r max -d rg ×(N rg -n),0≤n≤N rg –1

[0058] In the formula, r g (n) represents the nth uniformly spaced grid point, r max Indicates the maximum ground distance of the interference image, N rg Indicates the number of points evenly spaced from the grid, d rg Indicates evenly spaced spaces.

[0059] Step 3) performing linear interpolation on the interference image according to the ground distance grid generated in step 2);

[0060] During linear interpolation, the interpolation position is calculated by using a sliding window comparison method, that is, a comparison window is set to slide on the interference image, and the interpolation position is calculated by comparing the uniform ground distance grid with the pixel ground distance in the comparison window. The position of the comparison window changes continuously as the image interpolation proceeds, so that the interpolation position of each uniform ground distance grid is correctly calculated. Specifically, the following steps are included:

[0061] Step 3-1) Read the pixel distance covered by the current comparison window;

[0062] Step 3-2) comparing the uniform distance grid and the pixel distance;

[0063] Step 3-3) decoding the comparison result of step 3-3) and calculating the interpolation position of the current uniform distance grid;

[0064] Step 3-4) reading in the interference image pixels and pixel-to-pixel distances according to the interpolation positions calculated in step 3-3);

[0065] Step 3-5) completes the linear interpolation calculation;

[0066] The linear interpolation operation on the interference image is calculated as follows:

[0067]

[0068] In the formula, s g represents the interpolated pixel, r g represents a uniform ground distance grid, s0 represents the first pixel involved in the interpolation, r0 represents the ground distance of the first pixel, s1 represents the second pixel involved in the interpolation, and r1 represents the ground distance of the second pixel.

[0069] Step 3-6) uses the current interpolation position as the position of the next comparison window and returns to step 3-1).

[0070] Step 4) performing weighted averaging on the interference image processed in step 3);

[0071] Step 4) further comprises:

[0072] Step 4-1) dividing the interference image processed in step 3) into pixel neighborhoods;

[0073] Step 4-2) Obtain the weighted window coefficient f required for weighted averaging of interference images m,n ;

[0074] Step 4-3) uses the weighted window coefficient obtained in step 4-2) to perform weighted averaging on all pixel neighborhoods divided in step 4-1):

[0075]

[0076] In the formula, x m,n represents the interference image pixel involved in the pixel area, f m,n represents the weighted window coefficient, y represents the new pixel after weighted averaging, and M, N represent the number of points in the azimuth and distance directions of the pixel neighborhood.

[0077] Step 5) outputs the interference image processed in step 4).

[0078] The following is a further description of the steps in the method of the present invention by taking the interferometric image data obtained by the Tiangong-2 three-dimensional imaging microwave altimeter (TG-2InIRA) in the sea area near Cabo Verde on August 13, 2017 and the ground distance data corresponding to each pixel as an example.

[0079] In step 1), the interference image data and the ground distance of each pixel of the interference imaging altimeter are read in, and the data matrix is ​​256×1640.

[0080] Based on step 1), in step 2), the uniform ground distance grid point number N is first calculated according to the ground distance coverage range of the interference image in the embodiment. rg :

[0081]

[0082] In the formula, Indicates the rounding down operation, R g represents the ground distance coverage of the interferometric image, d rg Indicates evenly spaced distances;

[0083] Then, taking the maximum ground distance as a reference, generate a uniformly varying ground distance grid:

[0084] r g (n) = r max -d rg ×(N rg -n),0≤n≤N rg -1

[0085] In the formula, r g (n) represents the nth uniformly spaced grid point, r max Indicates the maximum ground distance of the interference image, N rg Indicates the number of points evenly spaced from the grid, d rg In this embodiment, the maximum ground distance r max It is 55550m.

[0086] Based on step 2), in step 3), the linear interpolation of the interference image is:

[0087]

[0088] In the formula, s g represents the interpolated pixel, r g represents a uniform ground distance grid, s0 represents the first pixel involved in the interpolation, r0 represents the ground distance of the first pixel, s1 represents the second pixel involved in the interpolation, and r1 represents the ground distance of the second pixel.

[0089] Based on step 3), in step 4), the interference image is weighted averaged:

[0090] First, the interference image at uniform ground distance is divided into pixel neighborhoods, and there is a certain overlap between adjacent pixel neighborhoods;

[0091] Then calculate the weighted window coefficient f required for the weighted average of the interference image m,n :

[0092] f m,n =f m ·f n

[0093] In the formula, f m Represents the weighted window coefficient corresponding to the azimuth, f n Represents the weighted window coefficient corresponding to the distance.

[0094] Azimuth weighted window coefficient f m The calculation method is as follows:

[0095]

[0096] Wherein, a0=0.35875, a1=0.48829, a2=0.14128, a3=0.01168, and M represents the number of azimuth points of the pixel neighborhood. In the embodiment, the number of azimuth points M of the pixel neighborhood is 27.

[0097] Distance weighted window coefficient f n The calculation method is as follows:

[0098]

[0099] Wherein, N represents the number of distance points in the pixel neighborhood. In the embodiment, the number of distance points N in the pixel neighborhood is 35.

[0100] Then, the obtained weighted window coefficient is used to perform weighted averaging on all pixel neighborhoods of the interference image:

[0101]

[0102] In the formula, x m,n represents the interference image pixels involved in a certain pixel area, f m,n represents the weighted window coefficient, y represents the new pixel after weighted averaging, and M, N represent the number of points in the azimuth and distance directions of the pixel neighborhood.

[0103] Based on the processing result of step 4), ie, the total interference image compression result, the processing result is displayed as an image in step 5).

[0104] Figure 2 The figure shows the unprocessed interference phase image in this embodiment, and the image size is 1640×256. It can be seen that there is a lot of Gaussian noise in the image, and the signal-to-noise ratio is low. Figure 3 The figure shows the interference phase image after interference image compression processing in this embodiment. The image size is 195×32. The data volume is greatly reduced compared with the unprocessed interference phase image. The data compression ratio reaches 67.28. The Gaussian noise in the image is basically filtered out, and the signal-to-noise ratio is significantly improved. Figure 4 The figure shows the height measurement error curves introduced by the method of the present invention and the block compression method commonly used in the field of synthetic aperture radar in the central azimuth line of the interference image in this embodiment. It can be seen that the height measurement error introduced by the block compression method at the close point can reach 50cm, and the height measurement error introduced at the far point fluctuates around 5cm, while the height measurement error introduced by the method of the present invention fluctuates around 0.1cm in the entire observation area. This verifies that the method of the present invention can solve the problem of the change of the ground distance resolution of the interferometric imaging altimeter and has the advantage of low error.

[0105] like Figure 5 As shown, embodiment 2 of the present invention proposes an interference image compression system for on-orbit processing of an interference imaging altimeter, including a processor, a Xilinx Vertix-7xc7vx690t series FPGA development board and an HDL program, and implements the method of embodiment 1 in a pipeline manner, including the following modules:

[0106] A data cache module, used to read and cache the interference image data and pixel ground distance of the interference imaging altimeter;

[0107] A grid generation module, used for generating a uniform ground distance grid according to the ground distance coverage of the interference image;

[0108] An image interpolation module is used to perform a linear interpolation operation on the interference image according to the uniformly spaced grid generated by the grid generation module;

[0109] The weighted average module performs weighted average calculation on the interference image processed by the image interpolation module and outputs the processing result.

[0110] In the data cache module, the interference image data and pixel distance in Example 1 are input in a pipeline manner.

[0111] Based on the data cache module, in the grid generation module, the uniformly spaced grid in Example 1 is generated in a pipeline manner.

[0112] Based on the grid generation module, in the image interpolation module, refer to Figure 6 , the interferometric image interpolation operation in Example 1 is implemented in a pipeline manner, and the interpolation position is calculated by sliding window comparison during the process, including the following steps:

[0113] Step 3-1) Read the pixel distance covered by the current comparison window;

[0114] Step 3-2) comparing the uniform distance grid and the pixel distance;

[0115] Step 3-3) decoding the comparison result of step 3-3) and calculating the interpolation position of the current uniform distance grid;

[0116] Step 3-4) reading in the interference image pixels and pixel-to-pixel distances according to the interpolation positions calculated in step 3-3);

[0117] Step 3-5) completing the linear interpolation calculation described in Example 1;

[0118] Step 3-6) Calculate the movement amount of the next comparison window according to the interpolation position changes of the most recent multiple clocks, thereby controlling the coverage of the next comparison window, and return to step 3-1);

[0119] Based on the image interpolation module, in the weighted average module, the interference image weighted average operation in Example 1 is implemented in a pipeline manner. The weighted window coefficient in Example 1 is calculated by the processor and injected into the internal storage area of ​​the FPGA, and the weighted average module directly reads the weighted window coefficient from the internal storage area to perform weighted average calculation.

[0120] Based on the weighted average module, the interference image compression result in Example 1 is output in a pipeline manner.

[0121] In the embodiment, the working clock of the FPGA development board is 100MHz, and the total time delay of the interference image compression is 4.494ms. Due to the full-process pipeline design, the system of the present invention occupies less resources, has a lower time delay, and has the advantages of low delay and low resource consumption. Although the embodiment uses a specific weighted window, the weighted window coefficient used in the system of the present invention can be directly changed by injection, which has the advantage that the weighted window is easy to change.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. An interference image compression method for on-orbit processing of an interference imaging altimeter, the method comprising: Step 1) reading in interference image data and pixel ground distance of an interference imaging altimeter; Step 2) generating a uniformly varying ground distance grid according to the ground distance coverage of the interference image; Step 3) performing linear interpolation on the interference image according to the generated ground distance grid; Step 4) performing weighted averaging on the interference image after the linear interpolation processing; Step 5) Output the interference image after weighted average processing.

2. The interference image compression method for on-orbit processing of an interference imaging altimeter according to claim 1, characterized in that: The step 2) comprises: Step 2-1) Calculate the number of points N that are evenly spaced from the grid rg : in, Indicates the rounding down operation; R g Indicates the ground distance coverage of the interference image; d rg Indicates evenly spaced distances; Step 2-2) Generate a uniformly varying ground distance grid using the maximum ground distance as a reference: r g (n)=r max -d rg ×(N rg -n),0≤n≤N rg -1 Among them, r g (n) represents the nth uniformly spaced grid point; r max Indicates the maximum ground distance of the interference image; N rg Indicates the number of points evenly spaced from the grid; d rg Indicates evenly spaced spaces.

3. The interference image compression method for on-orbit processing of an interference imaging altimeter according to claim 1, characterized in that: In the step 3), the interpolation position is calculated by using a sliding window comparison method, that is, a comparison window is set to slide on the interference image, and the interpolation position is calculated by comparing the uniform ground distance grid with the pixel ground distance in the comparison window. The position of the comparison window changes continuously as the image interpolation is performed, so that the interpolation position of each uniform ground distance grid is correctly calculated, including the following steps: Step 3-1) Read the pixel distance covered by the current comparison window; Step 3-2) comparing the uniform distance grid and the pixel distance; Step 3-3) decoding the comparison result and calculating the interpolation position of the current uniform distance grid; Step 3-4) Read in the interference image pixels and pixel-to-pixel distances according to the calculated interpolation positions; Step 3-5) completes the linear interpolation calculation; Step 3-6) uses the current interpolation position as the position of the next comparison window and returns to step 3-1).

4. The interference image compression method for on-orbit processing of an interference imaging altimeter according to claim 3, characterized in that: The linear interpolation calculation method is as follows: Among them, s g represents the interpolated pixel; r g Represents a uniform ground distance grid; s0 represents the first pixel involved in the interpolation; r0 represents the ground distance of the first pixel; s1 represents the second pixel involved in the interpolation; r1 represents the ground distance of the second pixel.

5. The interference image compression method for on-orbit processing of an interference imaging altimeter according to claim 1, characterized in that: The step 4) comprises: Step 4-1) dividing the interference image after linear interpolation processing into pixel neighborhoods; Step 4-2) Obtain the weighted window coefficient f required for weighted averaging of interference images m,n ; Step 4-3) Use the obtained weighted window coefficient to perform weighted averaging on all divided pixel neighborhoods: Among them, x m,n represents the interference image pixel involved in the pixel area; f m,n represents the weighted window coefficient; y represents the new pixel after weighted averaging; M, N represent the number of points in the azimuth and distance directions of the pixel neighborhood.

6. The interference image compression method for on-orbit processing of an interference imaging altimeter according to claim 1, characterized in that: The weighted window coefficient supports dynamic setting.

7. An interference image compression system for on-orbit processing of an interference imaging altimeter, implemented based on the method described in any one of claims 1 to 6, characterized in that: The system comprises: A data cache module, used to read and cache the interference image data and pixel ground distance of the interference imaging altimeter; A grid generation module, used for generating a uniform ground distance grid according to the ground distance coverage of the interference image; an image interpolation module, configured to perform a linear interpolation operation on the interference image according to the uniformly spaced grids generated by the grid generation module; and The weighted average module is used to perform weighted average calculation on the interference image processed by the image interpolation module and output the processing result.

8. The interference image compression system for on-orbit processing of an interference imaging altimeter according to claim 7, characterized in that: The system is implemented on FPGA and adopts a full-process pipeline design.

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