Synthetic aperture radar image quality evaluation method under inaccurate imaging parameter condition

Abstract

The invention relates to a synthetic aperture radar image quality evaluation method under the condition of inaccurate imaging parameters, and the method comprises the following steps: selecting a plurality of image quality evaluation functions, and carrying out the weighted summation to obtain a weighted minimum deviation evaluation function; and according to different to-be-measured imaging scenes, performing quality evaluation on the synthetic aperture radar images with inaccurate imaging parameters in the to-be-measured imaging scenes by adopting the weighted minimum deviation evaluation function of the corresponding optimal weighting coefficient. Compared with the prior art, the method has the advantages of high flexibility, high scene pertinence, capability of better reflecting image quality and the like.

Description

Technical Field

[0001] The invention relates to the technical field of synthetic aperture radar image evaluation, and in particular to a synthetic aperture radar image quality evaluation method under the condition of inaccurate imaging parameters. Background Art

[0002] The quality of synthetic aperture radar (SAR) images reflects the performance of the SAR system to a certain extent, and also determines the application efficiency of SAR to a large extent. Therefore, it is of great significance to evaluate the quality of SAR images. SAR images have unique problems such as speckle noise and ghosting, which are significantly different from optical images. Therefore, it is necessary to design a special image quality evaluation scheme for SAR. In the process of SAR imaging, many factors can lead to inaccurate imaging parameters, such as unstable motion of the platform carrying SAR, changes in meteorological conditions leading to changes in the characteristics of the electromagnetic wave transmission medium, etc. Imaging with inaccurate parameters will lead to deterioration of imaging results, which will affect the application of SAR images. At present, there is no special SAR image quality evaluation method for inaccurate imaging parameters. Therefore, when the imaging parameters are inaccurate, it is impossible to calibrate the imaging parameters to improve the imaging effect.

[0003] Existing SAR image quality assessment methods are mainly divided into two categories: subjective assessment methods and objective assessment methods. Subjective assessment methods evaluate image quality based on the subjective feelings of the viewer. The advantage is that they can directly reflect the feeling of the human eye and are more in line with reality. The disadvantage is that they are time-consuming and labor-intensive, and not easy to operate, and may be affected by the personal factors of the viewer. Objective assessment methods evaluate by calculating the objective quality indicators of the image in order to meet the requirements consistent with the subjective feelings of people. Traditional objective assessment methods mainly measure the quality of images from two aspects: point targets and surface targets. The evaluation indicators of point targets include: spatial resolution, peak sidelobe ratio, integrated sidelobe ratio, ambiguity, etc. The evaluation indicators of surface targets include: mean and variance, entropy, radiation resolution, equivalent view number, dynamic range, etc. These indicators can reflect the performance of the system, but they are not necessarily consistent with the feeling of the human eye. Subsequently, image quality assessment methods based on contrast, texture features, edge features, human visual system, etc. have been developed, such as Tenengrad criterion, HVSNR (Human Visual System Signal-to-Noise Ratio), etc. However, these methods are not proposed for the case of inaccurate imaging parameters, and do not consider the impact of the degree of deviation of imaging parameters from standard values ​​on imaging results. Some image quality assessment methods were selected and their evaluation performance under inaccurate imaging parameters was tested. The results show that the performance of these methods is not ideal. The specific work is as follows:

[0004] Four existing image quality assessment methods were selected for testing: variance, entropy, equivalent view count and Tenengrad criterion. A block of SAR raw echo data with known imaging parameters was found and imaged using the simplest two-dimensional matched filter algorithm. The parameters of the matched filter were deliberately deviated from the standard values, and images with different degrees of deviation were obtained. Then, the function values ​​corresponding to the four evaluation functions were calculated. The results are shown in the figure. Figure 1 As shown. Except for entropy, the other three evaluation functions have the larger the value, the better the image quality. In order to maintain this consistency, the entropy is taken as the opposite number. For ease of comparison, these values ​​are scaled so that all values ​​fall between 95 and 100. The ideal evaluation function should obtain the maximum value when the imaging parameter deviation is 0, the minimum value when the deviation is the largest, and the value decreases as the deviation increases, as shown in Figure 1 As shown by the blue line in Figure 1 From the above, we can see that none of the four evaluation functions have these characteristics. We tried other data and the results were not very satisfactory. As mentioned above, the traditional image quality assessment methods cannot correctly reflect the image quality when the imaging parameters are inaccurate. Therefore, it is necessary to propose an effective SAR image quality assessment method for inaccurate imaging parameters. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a synthetic aperture radar image quality assessment method under the condition of inaccurate imaging parameters, which can better reflect the image quality.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A synthetic aperture radar image quality assessment method under the condition of inaccurate imaging parameters comprises the following steps:

[0008] Select multiple image quality evaluation functions, and perform weighted summation to obtain a weighted minimum deviation evaluation function; according to different imaging scenes to be tested, use the weighted minimum deviation evaluation function with the corresponding optimal weighting coefficient to perform quality evaluation on synthetic aperture radar images with inaccurate imaging parameters in the imaging scenes to be tested;

[0009] The process of obtaining the optimal weighting coefficient includes: obtaining the original echo of the synthetic aperture radar and the corresponding imaging parameters in the current imaging scene, applying different deviation degrees to the imaging parameters, and obtaining multiple imaging images and corresponding standard function values;

[0010] The weighting coefficients in the weighted minimum deviation evaluation function are randomly generated, and each imaging image is evaluated according to the weighted minimum deviation evaluation function to obtain multiple function values, and the mean square error with the corresponding standard function value is calculated. The weighting coefficients are then iteratively optimized using an optimization algorithm to minimize the mean square error and obtain the optimal weighting coefficients in the current imaging scene.

[0011] Furthermore, the weighted minimum deviation evaluation function is a weighted sum of multiple image quality evaluation functions based on variance, entropy, equivalent view number, and Tenengrad criterion-based image quality evaluation functions.

[0012] Furthermore, the expression of the weighted minimum deviation evaluation function is:

[0013] WMD=c 1 ·V+c 2 ·H+c 3 ENL+c 4 ·TEN

[0014] Where WMD is the evaluation result of the weighted minimum deviation evaluation function, V is the evaluation result of the image quality evaluation function based on variance, H is the evaluation result of the image quality evaluation function based on entropy, ENL is the evaluation result of the image quality evaluation function based on equivalent view count, TEN is the evaluation result of the image quality evaluation function based on Tenengrad criterion, and c 1 is the first weighting coefficient, c 2 is the second weighting coefficient, c 3 is the third weighting coefficient, c 4 is the fourth weighting coefficient.

[0015] Furthermore, the weighted coefficients of the weighted minimum deviation evaluation function in different imaging scenarios are different.

[0016] Furthermore, the weighted coefficients in the weighted minimum deviation evaluation function are randomly generated in the interval [-1, 1].

[0017] Furthermore, the particle swarm algorithm is used to iteratively optimize the weighting coefficients according to the calculated mean square error.

[0018] Furthermore, the applying different degrees of deviation to the imaging parameters specifically includes: making the imaging parameters deviate from the accurate value by m%; the corresponding standard function value is 100-|m|.

[0019] Furthermore, the method also includes guiding the selection of corresponding imaging parameters according to the quality assessment result of the acquired synthetic aperture radar image to improve the imaging effect.

[0020] Furthermore, the imaging parameter corresponding to the maximum evaluation function value of the weighted minimum deviation evaluation function is taken as the true imaging parameter.

[0021] Furthermore, the various image quality evaluation functions selected are all objective evaluation methods for synthetic aperture radar image quality.

[0022] Compared with the prior art, the present invention has the following advantages:

[0023] (1) The present invention uses the weighted sum of several image quality evaluation functions as the evaluation index, and uses an optimization algorithm to determine the weighting coefficient; when the imaging parameters are inaccurate, the weighted minimum deviation evaluation function WMDEF can better reflect the image quality compared with the variance, entropy, equivalent view number and Tenengrad criterion.

[0024] The advantages of this method are: simple design concept, strong flexibility, and strong scene-specificity. Different image quality evaluation functions can be selected to calculate the weighted sum, different optimization algorithms can be used to determine the weighting coefficient, and different standard function values ​​can be selected to make it suitable for different imaging scenes. A specific weighting coefficient will be calculated for a specific scene so that it can well match the characteristics of the scene.

[0025] (2) The weighted minimum deviation evaluation function WMDEF of the present invention provides a new solution for SAR image quality assessment under inaccurate imaging parameters. WMDEF can be used for imaging parameter optimization and estimation under inaccurate imaging parameters. When the true imaging parameters are unknown, the currently used imaging parameters can be adjusted according to the evaluation results of WMDEF to achieve a better imaging effect. The true imaging parameters can be estimated by finding the imaging parameters corresponding to the maximum function value of WMDEF. Inspired by WMDEF, new image quality evaluation methods may be born in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the change of the values ​​of different evaluation functions provided in the background technology of the present invention with the deviation degree of imaging parameters;

[0027] Figure 2 A schematic flow chart of a synthetic aperture radar image quality assessment method under inaccurate imaging parameters provided in an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of a WMDEF coefficient calculation process provided in an embodiment of the present invention;

[0029] Figure 4 A schematic diagram of a WMDEF variation with imaging parameter deviation provided in an embodiment of the present invention;

[0030] Figure 5 It is a schematic diagram for comparing a WMDEF provided in an embodiment of the present invention with four other evaluation functions. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0032] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0034] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0035] Example 1

[0036] like Figure 2 As shown, this embodiment provides a synthetic aperture radar image quality assessment method under the condition of inaccurate imaging parameters, comprising the following steps:

[0037] S1: Select multiple image quality evaluation functions and perform weighted summation to obtain the weighted minimum deviation evaluation function (WMDEF);

[0038] S2: obtaining the original synthetic aperture radar echo and the corresponding imaging parameters in the current imaging scene, applying different deviation degrees to the imaging parameters, and obtaining multiple imaging images and corresponding standard function values;

[0039] S3: randomly generate weighting coefficients in the weighted minimum deviation evaluation function, evaluate each imaging image according to the weighted minimum deviation evaluation function, obtain multiple function values, and calculate the mean square error with the corresponding standard function value, so as to continuously iterate and optimize the weighting coefficients using an optimization algorithm to minimize the mean square error and obtain the optimal weighting coefficients in the current imaging scene;

[0040] S4: According to different imaging scenes to be tested, a weighted minimum deviation evaluation function with a corresponding optimal weighting coefficient is used to perform quality assessment on the synthetic aperture radar image with inaccurate imaging parameters in the imaging scene to be tested.

[0041] The weighted minimum deviation evaluation function WMDEF can select different image quality evaluation functions to calculate the weighted sum, such as selecting multiple from a variance-based image quality evaluation function, an entropy-based image quality evaluation function, an equivalent view number-based image quality evaluation function, and an image quality evaluation function based on the Tenengrad criterion. Different optimization algorithms can also be used to determine the weighting coefficients.

[0042] In this embodiment, the weighted minimum deviation evaluation function WMDEF is defined as the weighted sum of four image quality evaluation functions: variance (V), entropy (H), equivalent view number (ENL) and Tenengrad criterion (TEN), and the corresponding calculation expression is:

[0043] WMD=c 1 ·V+c 2 ·H+c 3 ENL+c 4 ·TEN

[0044] Where WMD is the evaluation result of the weighted minimum deviation evaluation function, V is the evaluation result of the image quality evaluation function based on variance, H is the evaluation result of the image quality evaluation function based on entropy, ENL is the evaluation result of the image quality evaluation function based on equivalent view count, TEN is the evaluation result of the image quality evaluation function based on Tenengrad criterion, and c 1 is the first weighting coefficient, c 2 is the second weighting coefficient, c 3 is the third weighting coefficient, c 4 is the fourth weighting coefficient; where the weighting coefficient c 1 ,c 2 ,c 3 ,c 4 Obtained by particle swarm algorithm.

[0045] The weighting coefficients in WMDEF vary according to the application scenario. If you want to use WMDEF in a certain scenario, you first need a set of imaging results with different deviations of imaging parameters in the scenario and select a standard function with the deviation as the independent variable to reflect the impact of the deviation results on the imaging.

[0046] For example, Figure 3 As shown in the figure, the standard deviation function selected is 100-|m|, where m is 100 times the degree of deviation, and the exact value of the imaging parameter is a. The imaging parameters deviate from the exact value by -5%, -4.5%, ... m%, respectively, to obtain n images. The specific method for calculating the weighting coefficient is as follows: First, four weighting coefficients in the interval [-1,1] are randomly generated, and then the function values ​​of WMDEF under different degrees of deviation are calculated, and the mean square error between these function values ​​and the standard function values ​​(these values ​​can be normalized if necessary) is calculated, and the particle swarm algorithm is used to continuously iterate the coefficients to minimize the mean square error. The coefficient obtained at this time is the coefficient of WMDEF in this scenario.

[0047] This embodiment also verifies the effectiveness of the method through experiments, and concludes that the method provides an effective solution for image quality assessment under inaccurate conditions, which is beneficial to improving image quality under such conditions.

[0048] The specific process is:

[0049] A set of data is used to test the performance of WMDEF. The WMDEF function value is plotted as the imaging parameter deviation changes. Figure 4 As shown, the changing trend of the function is in line with expectations.

[0050] In order to verify the universal applicability of WMDEF, this embodiment uses several groups of SAR raw echo data to perform the same imaging processing, and plots the changes of the function values ​​of WMDEF and other four image quality evaluation functions with the imaging parameter deviation. It is found that WMDEF can accurately reflect the change trend of image quality. One set of results is shown in FIG. Figure 5 shown.

[0051] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for assessing synthetic aperture radar image quality under inaccurate imaging parameters, characterized in that: The following steps are involved: Select multiple image quality evaluation functions, and perform weighted summation to obtain a weighted minimum deviation evaluation function; according to different imaging scenes to be tested, use the weighted minimum deviation evaluation function with the corresponding optimal weighting coefficient to perform quality evaluation on synthetic aperture radar images with inaccurate imaging parameters in the imaging scenes to be tested; The process of obtaining the optimal weighting coefficient includes: obtaining the original echo of the synthetic aperture radar and the corresponding imaging parameters in the current imaging scene, applying different deviation degrees to the imaging parameters, and obtaining multiple imaging images and corresponding standard function values; The weighting coefficients in the weighted minimum deviation evaluation function are randomly generated, and each imaging image is evaluated according to the weighted minimum deviation evaluation function to obtain multiple function values, and the mean square error with the corresponding standard function value is calculated. The weighting coefficients are then iteratively optimized using an optimization algorithm to minimize the mean square error and obtain the optimal weighting coefficients in the current imaging scene.

2. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The weighted minimum deviation evaluation function is a weighted sum of multiple image quality evaluation functions based on variance, entropy, equivalent view number, and Tenengrad criterion.

3. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 2, characterized in that: The expression of the weighted minimum deviation evaluation function is: WMD=c1·V+c2·H+c3·ENL+c4·TEN Wherein, WMD is the evaluation result of the weighted minimum deviation evaluation function, V is the evaluation result of the image quality evaluation function based on variance, H is the evaluation result of the image quality evaluation function based on entropy, ENL is the evaluation result of the image quality evaluation function based on equivalent view number, TEN is the evaluation result of the image quality evaluation function based on Tenengrad criterion, c1 is the first weighting coefficient, c2 is the second weighting coefficient, c3 is the third weighting coefficient, and c4 is the fourth weighting coefficient.

4. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The weighted coefficients of the weighted minimum deviation evaluation function in different imaging scenarios are different.

5. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The weighted coefficients in the weighted minimum deviation evaluation function are randomly generated in the interval [-1, 1].

6. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The particle swarm algorithm is used to iteratively optimize the weighting coefficients according to the calculated mean square error.

7. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The applying different deviation degrees to the imaging parameters specifically includes: making the imaging parameters deviate from the accurate value by m%; the corresponding standard function value is 100-|m|.

8. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The method also includes guiding the selection of corresponding imaging parameters according to the quality assessment result of the acquired synthetic aperture radar image to improve the imaging effect.

9. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The imaging parameter corresponding to the maximum evaluation function value of the weighted minimum deviation evaluation function is taken as the true imaging parameter.

10. The synthetic aperture radar image quality assessment method under inaccurate imaging parameters according to claim 1, characterized in that: The various image quality evaluation functions selected are all objective evaluation methods for synthetic aperture radar image quality.

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