Method for evaluating quality of sea surface wave spatial correlation of geosynchronous orbit sar

Abstract

The application discloses a geosynchronous orbit SAR sea surface wave imaging quality evaluation method and belongs to the technical field of geosynchronous orbit space correlation SAR sea surface wave imaging quality evaluation. The application obtains a sea surface wave echo signal, performs distance direction matching filtering and space correlation processing on the sea surface wave echo signal, generates an imaging result by using a back projection algorithm, calculates image intensity and a sea wave spectrum main energy direction slope, and finally quantifies imaging quality by using a space correlation evaluation factor. The evaluation factor can effectively represent the space coherence of sea surface wave imaging, and the larger the value is, the better the imaging quality is. The application solves the problem of insufficient applicability of existing evaluation methods in sea surface wave imaging due to low contrast and weak texture features, significantly improves the accuracy of geosynchronous orbit SAR sea surface wave imaging quality evaluation, and provides reliable technical support for marine environment monitoring.

Description

Technical Field

[0001] This invention relates to a method for assessing the spatial correlation quality of sea surface waves in geosynchronous orbit SAR, belonging to the technical field of imaging quality assessment of sea surface waves in geosynchronous orbit spatial correlation SAR. Background Technology

[0002] Sea surface wave (SSW) imaging quality assessment is a crucial research topic in the field of Synthetic Aperture Radar (SAR) imaging. It has profound implications for the development and evaluation of SSW imaging systems and provides key support for the optimization of SAR SSW imaging algorithms. Its importance is particularly pronounced in geostationary orbit SAR imaging. Geostationary orbit SAR enables continuous observation of large areas of the sea surface. However, due to the continuous motion of SSW waves, severe decorrelation occurs in the echo signals over the long synthetic aperture time of geostationary orbit SAR, significantly increasing the imaging difficulty. To address this challenge, geostationary orbit spatial correlation SAR imaging algorithms model, estimate, and compensate for the motion of SSW waves to reconstruct signal correlation over the long synthetic aperture time, thereby improving the effectiveness of slow-time accumulation and achieving high-precision SSW imaging. In this process, SSW imaging quality assessment is paramount. Only accurate imaging quality assessment can ensure the accuracy of the estimation of the spatiotemporal variations of SSW waves, thus guaranteeing subsequent motion compensation and imaging results.

[0003] After decades of development, SAR imaging quality assessment methods have made significant progress, and a variety of indicators for image quality assessment have been proposed. Typical evaluation factors include image intensity (Reference 1: Hu Kebin, Zhang Xiaoling, Shi Jun, et al. High-precision motion compensation method for SAR based on optimal image intensity[J]. Journal of Radar, 2015, 4(1): 60-69.) and image entropy (Reference 2: Chen Y, Li G, Zhang Q. Iterative minimum entropy algorithm for refocusing of moving targets in SAR images[J]. IET Radar, Sonar & Navigation, 2019, 13(8): 1279-1286.), which are widely used in SAR imaging focusing algorithms (Reference 3: Zeng T, Wang R, Li F. SAR image autofocus utilizing minimum-entropycriterion[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1552-1556.) to optimize the estimation and compensation of motion parameters.

[0004] However, current published literature lacks specific research on sea surface wave imaging quality. Existing imaging quality assessment methods are mainly applicable to high-contrast focused imaging of static land scenes or moving point targets. In contrast, sea surface wave imaging quality assessment faces greater challenges, primarily due to the complexity of wave motion, indistinct texture features, and low contrast. This limits the applicability of existing quality assessment factors, leading to a mismatch between imaging quality and assessment metrics. This inconsistency directly affects the accuracy of sea surface wave motion parameter estimation and compensation, thereby weakening the overall imaging performance of geostationary orbit space-correlated SAR imaging algorithms.

[0005] To address the aforementioned issues, this study aims to overcome the limitations of existing imaging quality assessment factors in low-contrast, weak-texture sea surface wave scenarios, and to design novel assessment factors and methods suitable for sea surface wave imaging quality evaluation, thereby improving the accuracy and stability of GEO SAR sea surface wave imaging. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing SAR image quality assessment factors in assessing the quality of geosynchronous orbit spatial correlation SAR sea surface wave imaging. It proposes an accurate assessment factor (spatial correlation assessment factor) and corresponding assessment method for evaluating the quality of geosynchronous orbit spatial correlation SAR sea surface wave imaging, which can significantly improve the accuracy of geosynchronous orbit spatial correlation SAR sea surface wave imaging quality assessment.

[0007] The technical solution of this invention:

[0008] A method for assessing the spatial correlation quality of sea surface waves from geosynchronous orbit SAR includes the following steps:

[0009] S1: Obtain the echo signal of sea surface waves by continuously illuminating the same sea surface area within the synthetic aperture time using a SAR satellite located in geosynchronous orbit.

[0010] S2: Perform range-direction matched filtering on the echo signal;

[0011] S3: Based on the geosynchronous orbit spatial correlation SAR imaging algorithm, the time-domain spatial correlation processing is performed on the matched filtering results corresponding to each position of the sea surface wave;

[0012] S4: The echo signal after spatial correlation processing is accumulated in a slow time by the back projection algorithm to obtain the imaging results at various positions on the imaging plane;

[0013] S5: Calculate the image intensity E based on the imaging results;

[0014] S6: Calculate the principal energy direction of the wave spectrum to obtain the slope k of the principal energy direction in the wave spectrum data;

[0015] S7: Calculate the spatial correlation assessment factor;

[0016] S8: Evaluate the imaging quality of geosynchronous orbit spatial correlation SAR sea surface waves based on the magnitude of the spatial correlation assessment factor. The larger the spatial correlation assessment factor, the better the imaging quality.

[0017] Specifically, in step S1, a signal is transmitted. Represented as:

[0018]

[0019] Where j is the imaginary number sign, Indicates a fast time. Represents a rectangular window function. Indicates the center frequency of the transmitted signal. Indicates frequency modulation of the transmitted signal;

[0020] The corresponding echo signal is represented as:

[0021]

[0022] in, Indicates echo time delay. This indicates the radar pattern.

[0023] Specifically, step S2 includes:

[0024] Matched filtering is performed on the distance direction using a systematic matching function, which is expressed as follows:

[0025] ;

[0026] Matched filtering in the fast time domain is performed in the frequency domain and implemented through a fast Fourier transform. The output of the matched filter is expressed as:

[0027]

[0028] in, Indicates slow time. This represents the distance response after matched filtering. This indicates the phase history.

[0029] Specifically, in step S3, for Place The coefficients of the spatial correlation processing of the matched filter output at time t are expressed as follows:

[0030]

[0031] Where c is the speed of light. and This represents the radial velocity and radial acceleration of the sea surface wave at the corresponding location. and These represent the satellite's radial velocity and radial acceleration, respectively. Represented as:

[0032] ;

[0033] Signal after spatial correlation processing Represented as:

[0034] .

[0035] Specifically, the imaging results in step S4 Represented as:

[0036]

[0037] in,

[0038] .

[0039] Specifically, the image intensity E in step S5 is represented as:

[0040] .

[0041] Specifically, step S6 includes:

[0042] S61: Perform a two-dimensional Fourier transform on the image to obtain the wave spectrum;

[0043] S62: Convert spectral data into a grayscale image;

[0044] S63: The Otsu method is used to calculate the global threshold of the grayscale image to determine the appropriate binarization segmentation point;

[0045] S64: Based on the calculated global threshold, the grayscale image is binarized to generate a binary image containing only non-zero pixels, i.e., high-energy regions, and zero pixels, i.e., low-energy regions.

[0046] S65: In a binary image, extract all non-zero pixels and use a line fitting method to calculate the principal direction slope of these pixels;

[0047] S66: The final output is the slope k of the principal energy direction in the spectral data.

[0048] Specifically, the formula for calculating the spatial correlation assessment factor D in step S7 is as follows:

[0049]

[0050] in, As a threshold, The activation function is expressed as:

[0051] .

[0052] The beneficial effects of this invention are:

[0053] This invention addresses the challenges of indistinct texture features and weak contrast in sea surface wave imaging under geostationary orbit spatial correlation SAR (GSAR) backgrounds. By combining the unique wave spectrum and image intensity of sea surface waves, it innovatively designs a spatial correlation evaluation factor. Based on this factor, the quality of sea surface wave imaging by the geostationary orbit spatial correlation SAR algorithm is accurately assessed. Compared with existing evaluation factors, the spatial correlation evaluation factor in this invention can significantly improve the accuracy of sea surface wave imaging quality assessment using geostationary orbit spatial correlation SAR. Attached Figure Description

[0054] Figure 1 A flowchart illustrating the implementation of a geostationary orbit SAR sea surface wave spatial correlation quality assessment method;

[0055] Figure 2 The simulated backscattering distribution of sea surface waves;

[0056] Figure 3 for Figure 2 Enlarged view of the backscattering distribution of sea surface waves in region A;

[0057] Figure 4 The spatial correlation factor represents the change in imaging quality as a function of velocity estimation error. (a) Comparison of evaluation factors under different velocity estimation errors, and (b) Image entropy under different velocity estimation errors.

[0058] Figure 5 The spatial correlation factor represents the change in imaging quality as a function of acceleration estimation error. (a) shows a comparison of evaluation factors under different acceleration estimation errors, and (b) shows the image entropy under different acceleration estimation errors. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The present invention is specifically implemented as follows:

[0061] The effectiveness of the proposed evaluation method is verified using simulated sea surface waves as an example. The radar and environmental parameters used in the simulation are shown in Table 1. The sea surface wave backscattering is calculated by combining Bragg scattering with tilt modulation and hydrodynamic modulation. The obtained sea surface backscattering is shown in Table 1. Figure 2 As shown.

[0062] Table 1

[0063] Radar parameters numerical values track semi-major axis 42164km Eccentricity 0.1 Perimeter Argument 90° track inclination 10° Angle of incidence 25° carrier frequency 5.3GHz FM bandwidth 5MHz Pulse repetition frequency 200Hz Environmental parameters numerical values wind speed 5m / s Wind 50km

[0064] S1: Sea surface wave echo signals are acquired by continuously illuminating the same sea surface area within the synthetic aperture time using a SAR satellite located in geosynchronous orbit. (Transmitted signal) Represented as:

[0065]

[0066] Where j is the imaginary number sign, Indicates a fast time. Represents a rectangular window function. Indicates the center frequency of the transmitted signal. This represents the frequency modulation of the transmitted signal. The corresponding echo signal is represented as:

[0067]

[0068] in, Indicates echo time delay. This indicates the radar pattern.

[0069] S2: Perform matched filtering on the range direction using the system matching function, which is expressed as follows:

[0070] ;

[0071] Matched filtering in the fast time domain is performed in the frequency domain and implemented through a fast Fourier transform. The output of the matched filter is expressed as:

[0072]

[0073] in, Indicates slow time. This represents the distance response after matched filtering. This indicates the phase history.

[0074] S3: Perform time-domain spatial correlation processing on the matched filter results corresponding to various positions of the sea surface wave to achieve motion compensation of the sea surface wave. Place The coefficients of the spatial correlation processing of the matched filter output at time t are expressed as follows:

[0075]

[0076] Where c is the speed of light. and This represents the radial velocity and radial acceleration of the sea surface wave at the corresponding location. and These represent the satellite's radial velocity and radial acceleration, respectively. Represented as:

[0077] ;

[0078] Signal after spatial correlation processing express:

[0079] .

[0080] S4: The echo signal after spatial correlation processing is accumulated over a slow time using a back projection algorithm to obtain the imaging results at various locations on the imaging plane. Represented as:

[0081]

[0082] in,

[0083] .

[0084] S5: To ensure the accuracy of processing, block evaluation is performed, with each sub-block occupying 9 and 11 resolution units in the range and azimuth directions, respectively.

[0085] S6: Image Intensity Calculated from the imaging results, and expressed as:

[0086] .

[0087] S7: Calculate the principal energy direction of the wave spectrum:

[0088] S71: Perform a two-dimensional Fourier transform on the image to obtain the wave spectrum;

[0089] S72: Convert the spectral data into a grayscale image for subsequent image processing;

[0090] S73: The Otsu method is used to calculate the global threshold of the grayscale image in order to determine the appropriate binarization segmentation point;

[0091] S74: Based on the calculated global threshold, the grayscale image is binarized to generate a binary image containing only non-zero pixels (high-energy regions) and zero pixels (low-energy regions);

[0092] S75: In a binary image, extract all non-zero pixels and use a line fitting method to calculate the principal direction slope of these pixels;

[0093] S76: Finally, the slope k of the principal energy direction in the spectral data is obtained.

[0094] S8: Spatial Correlation Assessment Factor The calculation formula is:

[0095]

[0096] in, As a threshold, The activation function is expressed as:

[0097] .

[0098] S9: Image quality assessment.

[0099] The imaging quality of sea surface waves in geosynchronous orbit space-correlated SAR depends on the velocity estimation error and the acceleration estimation error. The larger the estimation error, the blurrier the image and the worse the imaging quality of the sea surface waves. In the first sub-region A, as... Figure 3 As shown, the comparison between the proposed spatial correlation evaluation factor and traditional image quality evaluation factors (image intensity and image entropy) corresponding to different velocity and acceleration estimation errors is illustrated. Figure 4 and Figure 5 As shown. In practical applications, the imaging quality can be judged based on 90% of the maximum spatial correlation evaluation factor. If it exceeds 90% of the maximum spatial correlation evaluation factor, it is considered accurate, while if it is less than 90%, it is considered blurry.

[0100] Figure 4 and Figure 5 Spatial correlation factor pairs designed for this invention Figure 3 The neutron region sea surface wave imaging quality assessment curve describes the variation of the spatial correlation factor with the imaging quality represented by velocity and acceleration estimation errors. To highlight the advantages of the assessment factor designed in this invention, comparisons were made with image intensity and image entropy, respectively.

[0101] ① Comparison Evaluation Factor 1: Image Intensity

[0102] ② Comparison Evaluation Factor 2: Image Entropy ,in Represents grayscale level The probability distribution.

Claims

1. A method for evaluating the quality of the spatial correlation of sea surface waves in a geostationary SAR sea surface wave, characterized in that, Includes the following steps: S1: Obtain the echo signal of sea surface waves by continuously illuminating the same sea surface area within the synthetic aperture time using a SAR satellite located in geosynchronous orbit. wherein the transmitted signal in step S1 is represented as: ； Where j is the imaginary number sign, Indicates a fast time. Represents a rectangular window function. Indicates the center frequency of the transmitted signal. Indicates frequency modulation of the transmitted signal; The corresponding echo signal is represented as: ； in, Indicates echo time delay. Represents the radar pattern; S2: Perform range-direction matched filtering on the echo signal, including: Matched filtering is performed on the distance direction using a systematic matching function, which is expressed as follows: ； Matched filtering in the fast time domain is performed in the frequency domain and implemented through a fast Fourier transform. The output of the matched filter is expressed as: ； in, Indicates slow time. This represents the distance response after matched filtering. Indicates the phase history; S3: Based on the geosynchronous orbit spatial correlation SAR imaging algorithm, the time-domain spatial correlation processing is performed on the matched filtering results corresponding to each position of the sea surface wave; Among them, for Place The coefficients of the spatial correlation processing of the matched filter output at time t are expressed as follows: ； Where c is the speed of light. and This represents the radial velocity and radial acceleration of the sea surface wave at the corresponding location. and These represent the satellite's radial velocity and radial acceleration, respectively. Represented as: ； Spatial correlation processed signal Represented as: ； S4: The echo signal after spatial correlation processing is accumulated in a slow time by the back projection algorithm to obtain the imaging results at various positions on the imaging plane; The imaging results Represented as: ； in, ； S5: Calculate the image intensity E based on the imaging results; S6: Calculate the principal energy direction of the wave spectrum to obtain the slope k of the principal energy direction in the wave spectrum data; S7: Calculate the spatial correlation assessment factor; S8: Evaluate the imaging quality of geosynchronous orbit spatial correlation SAR sea surface waves based on the magnitude of the spatial correlation assessment factor. The larger the spatial correlation assessment factor, the better the imaging quality.

2. The method for assessing the spatial correlation quality of sea surface waves using geosynchronous orbit SAR according to claim 1, characterized in that, In step S5, the image intensity E is represented as: 。 3. The method for assessing the spatial correlation quality of sea surface waves using geosynchronous orbit SAR according to claim 2, characterized in that, Step S6 includes: S61: Perform a two-dimensional Fourier transform on the image to obtain the wave spectrum; S62: Convert spectral data into a grayscale image; S63: The Otsu method is used to calculate the global threshold of the grayscale image to determine the appropriate binarization segmentation point; S64: Based on the calculated global threshold, the grayscale image is binarized to generate a binary image containing only non-zero pixels, i.e., high-energy regions, and zero pixels, i.e., low-energy regions. S65: In a binary image, extract all non-zero pixels and use a line fitting method to calculate the principal direction slope of these pixels; S66: The final output is the slope k of the principal energy direction in the spectral data.

4. The method for assessing the spatial correlation quality of sea surface waves using geosynchronous orbit SAR according to claim 3, characterized in that, The formula for calculating the spatial correlation assessment factor D in step S7 is as follows: ； in, For image intensity, As a threshold, The activation function is expressed as: 。

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