An interferogram compression method and system for in-orbit processing of interferometric altimeters

Abstract

The application provides an interferometric image compression method and system for on-orbit processing of an interferometric imaging altimeter, the method comprising: reading in interferometric image data and pixel ground distances of the interferometric imaging altimeter; generating a uniformly changing ground distance grid according to a ground distance coverage range of the interferometric image; performing linear interpolation on the interferometric image according to the generated ground distance grid; performing weighted averaging on the processed interferometric image; and outputting the processed interferometric image. The method of the application can realize high-ratio interferometric image compression while ensuring high precision of the interferometric imaging altimeter, and can improve spatial frequency spectrum information of the interferometric image by using window weighting. The application realizes an FPGA system based on a pipeline design, has the advantages of low time delay, low resource consumption and easy change of the weighted window, and can meet actual application requirements of interferometric image compression in on-orbit processing of the interferometric imaging altimeter.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of interferometric imaging altimeter, and particularly relates to an interferogram image compression method and system for in-orbit processing of an interferometric imaging altimeter. BACKGROUND

[0002] The interferometric imaging altimeter is a new generation of spaceborne radar altimeter, which can realize wide swath, high resolution and high precision sea surface height measurement. Due to the full-time working system, the original data volume of the interferometric imaging altimeter is huge. In order to alleviate the pressure of data transmission from space to ground, it is necessary to greatly reduce the data volume while maintaining the height measurement accuracy. Direct data compression of echo data using general data compression methods will introduce large errors to the height measurement, which cannot meet the height measurement accuracy requirements. Therefore, it is necessary to perform in-orbit processing on the echo data to obtain the interferogram image, and to realize high-rate 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 take the average, which reduces the data volume and improves the signal-to-noise ratio by sacrificing the image resolution. However, the range resolution of the interferometric imaging altimeter changes along the distance direction, and the range resolution of the near-range point of the interferogram image is much lower than that of the far-range point. The above-mentioned direct block compression method of the interferogram image will introduce large interferometric phase errors at the near-star lower point, which also cannot meet the height measurement accuracy requirements. In order to compress the interferogram image while maintaining the height measurement accuracy, it is necessary to solve the problem of the change of the range resolution of the interferometric imaging altimeter. Therefore, a new interferogram image compression method and system are proposed to efficiently realize in-orbit compression of the interferogram image, which has important significance for the research of the next generation of interferometric imaging altimeter. SUMMARY

[0003] The present application aims to overcome the defects of interferogram image compression in the in-orbit processing of the interferometric imaging altimeter, reduce the influence of interferogram image compression on the height measurement accuracy, improve the utilization efficiency of on-board hardware resources, and realize low-error, low-latency and low-resource consumption in-orbit compression of the interferogram image.

[0004] In order to achieve the above-mentioned purpose, the present application proposes an interferogram image compression method for in-orbit processing of an interferometric imaging altimeter, which comprises:

[0005] Step 1) reading in the interferogram image data and pixel range of the interferometric imaging altimeter;

[0006] Step 2) generating a uniformly changing range grid according to the range coverage of the interferogram image;

[0007] Step 3) performing linear interpolation on the interferogram image according to the generated range grid;

[0008] Step 4) performing weighted averaging on the linearly interpolated interferogram image;

[0009] Step 5) output the weighted average processed interference image.

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

[0011] Step 2-1) calculate the number of points N of the uniform distance grid rg :

[0012]

[0013] wherein, represents the floor operation; R g represents the distance coverage of the interference image; d rg represents the uniform distance interval.

[0014] Step 2-2) generate a uniform distance grid with the maximum distance as a reference:

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

[0016] wherein, r g (n) represents the nth point of the uniform distance grid; r max represents the maximum distance of the interference image; N rg represents the number of points of the uniform distance grid; d rg represents the uniform distance interval.

[0017] As an improvement of the above method, in the step 3), the interpolation position is calculated by using the sliding window comparison method, that is, a comparison window sliding on the interference image is set, the interpolation position is calculated by comparing the uniform distance grid with the pixel distance in the comparison window, and the position of the comparison window is changed constantly with the image interpolation, so that the interpolation position of each uniform distance grid is calculated correctly, comprising the following steps:

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

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

[0020] Step 3-3) decode the comparison result to calculate the interpolation position of the current uniform distance grid;

[0021] Step 3-4) read the interference image pixel and the pixel distance according to the calculated interpolation position;

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

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

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

[0025]

[0026] Wherein, s g represents the interpolated pixel; r g represents the uniform distance from the grid; s0 represents the first pixel involved in the interpolation; r0 represents the distance from the first pixel; s1 represents the second pixel involved in the interpolation; and r1 represents the distance from the second pixel.

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

[0028] Step 4-1) divide the interference image processed by linear interpolation into pixel neighborhoods;

[0029] Step 4-2) obtain the weighting window coefficient f m,n needed for the weighted average of the interference image;

[0030] Step 4-3) perform weighted average on all pixel neighborhoods divided by using the obtained weighting window coefficient:

[0031]

[0032] Wherein, x m,n represents the interference image pixel involved in the pixel field; f m,n represents the weighting window coefficient; y represents the new pixel after weighted average; and M, N represent the point number of the pixel neighborhood in the direction and distance.

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

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

[0035] A data caching module for reading and caching interference image data and pixel distances of the interferometric imaging altimeter;

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

[0037] An image interpolation module for performing linear interpolation operation on the interference image according to the uniform distance grid generated by the grid generation module;

[0038] The weighted average module is configured 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 an FPGA and adopts a full-process pipeline design.

[0040] Compared with the prior art, the application has the following advantages:

[0041] The application adopts a pipeline design, interpolates the interference image to a uniform grid, re-compresses the interference image, and improves the spatial spectrum information of the interference image through window weighting. Based on the above method and system, the application can eliminate the influence of the rapid change of the range resolution of the interferometric altimeter, ensure the height measurement accuracy of the compressed interference image in orbit, and has the advantages of low time delay, low resource consumption, and easy change of the weighting window, thereby meeting the actual application requirements of the interference image compression in the on-orbit processing of the interferometric altimeter. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 Fig. 1 shows a flowchart of the interference image compression method for on-orbit processing of an interferometric altimeter;

[0043] Figure 2 Fig. 2 shows an interference phase image of measured data of an interferometric altimeter without interference image compression processing;

[0044] Figure 3 Fig. 3 shows an interference phase image of measured data of an interferometric altimeter after the interference image compression processing by the method of the application;

[0045] Figure 4 Fig. 4 shows a height measurement error curve diagram respectively introduced by the method of the application and the block compression method in the center azimuth line of the interference image;

[0046] Figure 5 Fig. 5 shows an implementation block diagram of an interference image compression system for on-orbit processing of an interferometric altimeter;

[0047] Figure 6 Fig. 6 shows a work flowchart of the image interpolation module in the system of the application. DETAILED DESCRIPTION

[0048] The technical solutions of the application will be described in detail below with reference to the accompanying drawings.

[0049] As Figure 1 shown, the application provides an interference image compression method for on-orbit processing of an interferometric altimeter, which includes the following steps:

[0050] Step 1) reading in the interferometric image data and the pixel distances of the interferometric height meter;

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

[0052] Step 2) further comprises:

[0053] Step 2-1) calculating the number N of points of the grid of uniformly varying distances rg :

[0054]

[0055] wherein, denotes a floor operation, R g denotes the distance coverage of the interferometric image, d rg denotes the uniformly varying distance interval.

[0056] Step 2-2) generating the grid of uniformly varying distances with reference to the maximum distance:

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

[0058] wherein, r g (n) denotes the n-th point of the grid of uniformly varying distances, r max denotes the maximum distance of the interferometric image, N rg denotes the number of points of the grid of uniformly varying distances, d rg denotes the uniformly varying distance interval.

[0059] Step 3) linearly interpolating the interferometric image according to the grid of distances generated in step 2);

[0060] In the linear interpolation, the interpolation position is calculated by means of a sliding window comparison, i.e. a comparison window is set to slide on the interferometric image, the interpolation position is calculated by comparing the grid of uniformly varying distances with the pixel distances within the comparison window, the position of the comparison window is constantly changed as the image interpolation proceeds, so that the interpolation position of each point of the grid of uniformly varying distances is correctly calculated, which comprises the following steps:

[0061] Step 3-1) reading in the pixel distances covered by the current comparison window;

[0062] Step 3-2) comparing the grid of uniformly varying distances with the pixel distances;

[0063] Step 3-3) decoding the comparison result of step 3-3) to calculate the interpolation position of the current point of the grid of uniformly varying distances;

[0064] Step 3-4) read in the interferogram image pixels and pixel ground range according to the interpolation position calculated in step 3-3);

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

[0066] The linear interpolation operation calculation method for the interferogram image is as follows:

[0067]

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

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

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

[0071] Step 4) further comprises:

[0072] Step 4-1) divide the pixel neighborhood of the interferogram image processed in step 3);

[0073] Step 4-2) obtain the weighting window coefficient f m,n required for the weighted averaging of the interferogram image;

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

[0075]

[0076] In the formula, x m,n represents the interferogram image pixel involved in the pixel neighborhood, f m,n represents the weighting window coefficient, y represents the new pixel after weighted averaging, and M and N represent the point numbers in the azimuth direction and the range direction of the pixel neighborhood.

[0077] Step 5) output the interferogram image processed in step 4).

[0078] The following takes the interferogram image data and the ground range data corresponding to each pixel acquired by the Tiangong-2 three-dimensional imaging microwave altimeter (TG-2 InIRA) near the waters of Cabo Verde on August 13, 2017 as an example to further describe the steps in the method.

[0079] In step 1), the interference image data and the pixel distance of the interference imager are read in, and the data matrix is 256x1640.

[0080] Based on step 1), in step 2), the number of uniform distance grid points N is calculated according to the distance coverage of the interference image in the example rg :

[0081]

[0082] In the formula, represents the floor operation, R g represents the distance coverage of the interference image, d rg represents the uniform distance interval.

[0083] Then, taking the maximum distance as a reference, a uniform distance grid is generated:

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

[0085] In the formula, r g (n) represents the nth uniform distance grid point, r max represents the maximum distance of the interference image, N rg represents the number of uniform distance grid distance points, d rg represents the uniform distance interval. In this example, the maximum distance r max is 55550m.

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

[0087]

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

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

[0090] First, the pixel neighborhood of the interference image under the uniform distance is divided, and there is a certain overlap between adjacent pixel neighborhoods.

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

[0092] f m,n = f m · f n

[0093] wherein f m represents a corresponding azimuthal direction weighted window coefficient, and f n represents a corresponding radial direction weighted window coefficient.

[0094] The azimuthal direction weighted window coefficient f m is calculated as follows:

[0095]

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

[0097] The radial direction weighted window coefficient f n is calculated as follows:

[0098]

[0099] wherein N represents the number of radial direction points of the pixel neighborhood. In the embodiment, the number of radial direction points N of the pixel neighborhood is 35.

[0100] Then, the weighted average of all pixel neighborhoods of the interference image is performed by using the obtained weighted window coefficients:

[0101]

[0102] wherein x m,n represents a pixel involved in a certain pixel neighborhood, f m,n represents the weighted window coefficient, y represents the new pixel after the weighted average, and M, N represent the number of azimuthal direction and radial direction points of the pixel neighborhood.

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

[0104] Figure 2 Fig. 1 shows the interference phase image without processing in the embodiment, and the image size is 1640x256. It can be seen that there are a large number of Gaussian noises in the image, and the signal-to-noise ratio is low. Figure 3 Fig. 2 shows the interference phase image after the interference image compression processing in the embodiment, and the image size is 195x32. The data amount is greatly reduced compared with the interference phase image without processing, the data compression ratio is 67.28, and the Gaussian noises in the image are basically filtered out, and the signal-to-noise ratio is significantly improved.Figure 4 The figure shows the height error curves introduced by the block compression method commonly used in the field of synthetic aperture radar and the method of the present application in the central azimuth line of the interferogram image. It can be seen that the block compression method introduces a height error of up to 50 cm at the near range point, and the height error introduced at the far range point is floating around 5 cm, while the height error introduced by the method of the present application is floating around 0.1 cm in the whole observation area. Thus it is verified that the method of the present application can solve the problem of varying range resolution of interferometric imaging altimeter, and has the advantage of low error.

[0105] As Figure 5 shown, embodiment 2 of the present application proposes an interferogram compression system for on-orbit processing of interferometric imaging altimeter, which includes a processor, a Xilinx Vertix-7 xc7vx690t series FPGA development board and a HDL program, to realize the method of embodiment 1 in a pipeline manner, including the following modules:

[0106] A data caching module for reading and caching the interferogram data and pixel range of the interferometric imaging altimeter;

[0107] A grid generation module for generating a uniform range grid according to the range coverage of the interferogram image;

[0108] An image interpolation module for performing linear interpolation operation on the interferogram image according to the uniform range grid generated by the grid generation module;

[0109] A weighted average module for performing weighted average calculation on the interferogram image processed by the image interpolation module and outputting the processing result.

[0110] In the data caching module, the interferogram data and pixel range in embodiment 1 are input in a pipeline manner.

[0111] Based on the data caching module, the uniform range grid in embodiment 1 is generated in the grid generation module in a pipeline manner.

[0112] Based on the grid generation module, in the image interpolation module, referring to Figure 6 , the interferogram interpolation operation in embodiment 1 is realized in a pipeline manner, and in this process, the interpolation position is calculated by sliding window comparison, including the following steps:

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

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

[0115] Step 3-3) decoding the comparison result of step 3-3) to calculate the interpolation position of the current uniform range grid;

[0116] Step 3-4) According to the interpolation position calculated in step 3-3), read in the interferogram image pixels and pixel distances;

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

[0118] Step 3-6) According to the change of the interpolation position of the nearest multiple clocks, calculate the moving amount of the next comparison window, so as to control the coverage of the next comparison window, return to step 3-1);

[0119] Based on the image interpolation module, the weighted average operation of the interferogram in example 1 is realized in a pipeline manner in the weighted average module. The weighted window coefficients in example 1 are calculated by the processor and injected into the internal storage area of the FPGA, and the weighted average module directly reads the weighted window coefficients from the internal storage area for weighted average calculation.

[0120] Based on the weighted average module, the compressed result of the interferogram in example 1 is output in a pipeline manner.

[0121] The working clock of the FPGA development board in the example is 100MHz, and the total time delay of the interferogram compression is 4.494ms. Due to the pipeline design of the whole process, the system of the present application occupies less resources and has lower time delay, which has the advantages of low time delay and low resource consumption. Although the example selects a specific weighted window, the weighted window coefficients used in the system of the present application can be changed directly by injection, which has the advantage of easy change of the weighted window.

[0122] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the examples, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for compressing interferometric images for on-orbit processing of interferometric imaging altimeters, the method comprising: Step 1) Read in the interferometric image data and pixel distance from the interferometric imaging altimeter; Step 2) Generate a uniformly varying ground distance grid based on the ground distance coverage of the interferometric image; Step 3) Perform linear interpolation on the interferometric image according to the generated ground distance grid; Step 4) Perform a weighted average on the interferometric image after linear interpolation; Step 5) Output the weighted average processed interferometric image; In step 3), the interpolation position is calculated using a sliding window comparison method. This involves setting a comparison window that slides across the interferometric image. The interpolation position is calculated by comparing the uniformly spaced grid with the pixel distance within the comparison window. The position of the comparison window changes continuously as the image interpolation proceeds, ensuring that the interpolation position of each uniformly spaced grid is correctly calculated. This includes the following steps: Step 3-1) Read in the pixel distances covered by the current comparison window; Step 3-2) Compare the uniform ground distance grid and the pixel ground distance; Step 3-3) Decode the comparison results and calculate the current uniform distance from the grid; Steps 3-4) Read in the interferometric image pixels and pixel distances according to the calculated interpolation positions; Steps 3-5) Complete the linear interpolation calculation; Step 3-6) Use the current interpolation position as the position of the next comparison window and return to step 3-1); The linear interpolation calculation method is as follows: Among them, s g Indicates the interpolated pixel; r g The grid represents uniform distance; s0 represents the first pixel involved in the interpolation; r0 represents the distance of the first pixel; s1 represents the second pixel involved in the interpolation; r1 represents the distance of the second pixel.

2. The interferometric image compression method for on-orbit processing of interferometric imaging altimeters according to claim 1, characterized in that, Step 2) includes: Step 2-1) Calculate the number N of points uniformly spaced from the grid. rg : in, Indicates the floor operation; R g Indicates the ground distance coverage of the interferometric image; d rg Indicates a uniform spacing; Step 2-2) Using the maximum ground distance as a reference, generate a uniformly varying ground distance grid: r g (n)=r max -d rg ×(N rg -n),0≤n≤N rg -1 Where, r g (n) represents the nth uniformly spaced grid point; r max N represents the maximum ground distance in the interferometric image; rg d represents the distance from the grid points uniformly; rg It indicates a uniform spacing.

3. The interferometric image compression method for on-orbit processing of interferometric imaging altimeters according to claim 1, characterized in that, Step 4) includes: Step 4-1) Divide the interferometric image after linear interpolation into pixel neighborhoods; Step 4-2) Obtain the weighted window coefficient f required for weighted averaging of the interferometric image. m,n ; Step 4-3) Calculate a weighted average of all pixel neighborhoods using the obtained weighted window coefficients: Where, x m,n f represents the interferometric image pixels involved in the pixel region; m,n y represents the weighted window coefficient; y represents the new pixel after weighted averaging; M and N represent the number of azimuth and distance points in the neighborhood of the pixel.

4. The interferometric image compression method for on-orbit processing of interferometric imaging altimeters according to claim 1, characterized in that, The weighted window coefficients can be dynamically set.

5. An interferometric image compression system for on-orbit processing of an interferometric imaging altimeter, implemented based on the method described in any one of claims 1-4, characterized in that, The system includes: The data caching module is used to read in and cache the interferometric image data and pixel ground distance of the interferometric imaging altimeter; The grid generation module is used to generate a uniform ground distance grid based on the ground distance coverage of the interferometric image; The image interpolation module performs linear interpolation on the interferometric image according to a uniformly spaced grid generated by the grid generation module; and The weighted average module is used to calculate the weighted average of the interferometric image processed by the image interpolation module and output the processing result.

6. The interferometric image compression system for on-orbit processing of an interferometric imaging altimeter according to claim 5, characterized in that, The system is implemented on an FPGA and adopts a fully pipelined design.

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