A Method for Detecting and Suppressing Azimuth Blur in Spaceborne SAR Scene Matching Curve Imaging

Abstract

This invention discloses a method for detecting and suppressing azimuth blur in spaceborne SAR scene matching curve imaging, comprising: 1. Modeling the azimuth blur, providing analytical expressions for the azimuth blur echo, range migration correction, and azimuth phase compensation terms; 2. Refocusing the nth-order azimuth blur based on the range migration correction and azimuth phase compensation terms; 3. Calculating the entropy difference between the SAR image and the nth-order blurred refocused image in blocks, and using the CFAR algorithm to detect blurred regions; 4. Filtering using a Wiener filter to generate a low-blur, low-resolution image; and combining the azimuth blur region detection results, replacing the detected sub-blocks to obtain a low-blur, high-resolution image. This invention can solve the azimuth blur problem in spaceborne SAR scene matching curve imaging mode, effectively improving image quality while preserving the resolution of the original image.

Description

Technical Field

[0001] This invention belongs to the technical field of Synthetic Aperture Radar (SAR), specifically relating to a method for detecting and suppressing azimuth ambiguity in spaceborne SAR scene matching curve imaging. Background Technology

[0002] Azimuth ambiguity is a crucial indicator for evaluating the quality of spaceborne SAR images. Traditional spaceborne SAR imaging modes follow the satellite orbit, resulting in a uniform geographic orientation. However, in scene-matched curve imaging modes, the imaging band may not be parallel to the satellite orbit, leading to a more complex and variable geographic orientation. Because the antenna pattern of a spaceborne SAR contains sidelobe energy in addition to the energy required for the observation area, echo power from the sidelobe regions enters the main lobe observation area, creating ambiguity energy. This unwanted ambiguity energy overlaps with the main image area energy, affecting the quality of high-resolution SAR imaging and the effectiveness of Earth observation. In scene-matched curve imaging, scene orientation, echo delay, and Doppler history vary drastically with azimuth time. When the azimuth ambiguity is poor, the antenna sidelobe energy significantly interferes with the SAR image, leading to false targets, severely degrading image quality, and greatly limiting the further interpretation and application of spaceborne SAR images. In short, suppressing azimuth ambiguity in scene-matched curve imaging SAR images can improve SAR image quality and interpretability.

[0003] Conventional azimuth blur suppression methods mostly employ post-signal processing to suppress azimuth blur in traditional spaceborne SAR imaging modes, such as stripe modes. These methods are poorly adapted to spatial variations in scene configuration and Doppler history. Furthermore, suppressing azimuth blur solely through signal processing often results in resolution loss, further degrading image quality. Therefore, it is necessary to deeply analyze the azimuth blur characteristics in spaceborne SAR non-track scene matching curve imaging modes, study the formation mechanism of azimuth blur, and develop low-resolution loss suppression signal processing algorithms to address the azimuth blur problem. Summary of the Invention

[0004] In view of this, the present invention provides an azimuth blur suppression algorithm for low resolution loss in spaceborne SAR scene matching curve imaging, which can solve the azimuth blur problem in spaceborne SAR scene matching curve imaging mode, effectively improve the SAR image quality while preserving the resolution of the original image.

[0005] The technical solution for implementing the present invention is as follows:

[0006] A method for detecting and suppressing azimuth ambiguity in spaceborne SAR scene matching curve imaging includes the following steps:

[0007] Step 1: Model the azimuth ambiguity, and give the analytical expression of the azimuth ambiguity echo, the range migration correction compensation term, and the azimuth phase compensation term respectively;

[0008] Step 2: Based on the range migration correction compensation term and the azimuth phase compensation term, refocus the nth-order azimuth blur on the SAR image;

[0009] Step 3: Calculate the image entropy difference between the SAR image and the nth-order azimuth-blurred refocused image by dividing them into blocks, and use the CFAR algorithm to complete the detection of azimuth-blurred regions.

[0010] Step 4: Filter the SAR image using a Wiener filter to generate a low-blurred, low-resolution image; combine the results of the azimuth-blurred region detection, and replace the detected sub-blocks to obtain a low-blurred, high-resolution image, thus completing the blur suppression.

[0011] Furthermore, the distance migration correction compensation term and the azimuth phase compensation term mentioned in step two are respectively:

[0012]

[0013] Where R is the reference slope distance, R am f is the reference slant distance for the fuzzy region. η For the azimuth frequency, ΔR(f) η ) represents the distance migration correction compensation item. This is the azimuth phase compensation term.

[0014] Furthermore, the image entropy mentioned in step three is:

[0015]

[0016] Where I(x,y) is the gray value of the image, S is the number of times different gray values ​​appear in the image, and M and N are the total number of sample points in the azimuth and distance directions of the image.

[0017] Furthermore, in step three, the CFAR detection method is used to detect the change in entropy difference before and after refocusing, thereby achieving the detection of targets in the main blurred region.

[0018] ΔE=E refoused -E original (3)

[0019] Among them, E refoused E represents the image entropy after refocusing. original ΔE represents the initial SAR image entropy, and ΔE represents the entropy difference before and after processing.

[0020] Furthermore, the Wiener filter mentioned in step four is:

[0021]

[0022] Wherein, H(f) η ) is an azimuth frequency domain filter, f η Let σ be the azimuth frequency, and σ0 be the backscattering coefficients of the main image target and the blurred target, respectively. t G is the incandescent noise power density, and G(·) is the antenna gain.

[0023] Beneficial effects:

[0024] 1. This invention solves the problem of azimuth ambiguity in non-track scene matching curve imaging of spaceborne SAR, which can effectively improve SAR image quality and suppress azimuth ambiguity.

[0025] 2. Steps one and two of this invention provide the analytical expression for the azimuth fuzzy echo in this mode, the range migration correction compensation term, and the azimuth phase compensation term, which solves the problem of focusing the azimuth fuzzy echo and lays the research foundation for subsequent detection and suppression.

[0026] 3. Step three of this invention proposes a blurry region detection algorithm based on image entropy difference, which can complete blurry region detection under the condition of azimuth blur and spatial variation;

[0027] 4. Step four of this invention proposes a blur suppression algorithm based on low blur map replacement. After processing with Wiener filtering, a low blur and low resolution image can be obtained. The low blur map is replaced using the azimuth blur detection result to obtain an overall high resolution and low blur SAR image, thus achieving azimuth blur suppression with low resolution loss. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method for detecting and suppressing azimuth ambiguity in spaceborne SAR scene matching curve imaging as described in this invention;

[0029] Figure 2 This is a schematic diagram of the cause of azimuth ambiguity in non-track scene matching curve imaging of spaceborne SAR as described in this invention; (a) geometric configuration of azimuth ambiguity in spaceborne SAR, (b) energy aliasing of azimuth ambiguity in Doppler domain;

[0030] Figure 3 This is a schematic diagram illustrating the formation of azimuth blur in conventional range-Doppler domain imaging algorithms;

[0031] Figure 4 This is a schematic diagram of the azimuth blur focusing imaging method proposed in this invention;

[0032] Figure 5These are schematic diagrams of ±1st order azimuth blur focusing imaging proposed in this invention; (a) schematic diagram of +1st order azimuth blur distance migration correction, (b) schematic diagram of -1st order azimuth blur distance migration correction, (c) +1st order azimuth blur focusing imaging, (d) -1st order azimuth blur focusing imaging, (e) +1st order azimuth blur focusing imaging azimuth peak-to-sidelobe ratio, and (f) -1st order azimuth blur focusing imaging azimuth peak-to-sidelobe ratio.

[0033] Figure 6 The following is a schematic diagram of the SAR image after suppressing azimuth blur in the embodiment of the present invention: (a) SAR image before suppressing azimuth blur, (b) SAR image before suppressing azimuth blur, (c) SAR image after suppressing azimuth blur, and (d) SAR image after suppressing azimuth blur. Detailed Implementation

[0034] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] The flowchart of the method for azimuth ambiguity detection and suppression in spaceborne SAR scene matching curve imaging described in this invention is as follows: Figure 1 As shown, the present invention includes the following steps:

[0036] Step 1: Model the azimuth ambiguity and give the analytical expressions for the azimuth ambiguity echo;

[0037] In spaceborne SAR, the same echo transmission and reception are separated by several PRTs. Figure 2 Taking the first ambiguous region in (a) as an example, the radar receiver simultaneously receives echoes from both the ambiguous region and the main imaging region. During satellite travel, these three regions will form continuous Doppler frequencies, exceeding the system's sampling bandwidth. Since the system performs azimuth discrete sampling according to PRF in the azimuth direction, aliasing will occur within the sampling bandwidth. The aliased signal is focused by the imaging processor, ultimately forming azimuth ambiguity in the SAR image. When unaffected by in-azimuth weighting, the single-pass beam pattern of the spaceborne SAR antenna can be approximated as a sinc function related to the angle θ:

[0038]

[0039] In the formula, G(·) represents the echo azimuth gain, and L a λ represents the azimuth dimension of the antenna, λ represents the radar carrier frequency wavelength, and θ represents the incident angle.

[0040] The demodulated signal received from a point target in a SAR can be written as:

[0041]

[0042] The slant distance R(η) at different azimuths and times is:

[0043]

[0044] Where τ and η are the azimuth time and range time, respectively, σ0 is the point target backscattering coefficient, and η c V is the moment of beam center crossing. r R0 is the equivalent radar velocity, C is the shortest slant range, and K is the speed of light. r For the range-directed frequency modulation, w(·) represents the rectangular envelope.

[0045] In spaceborne SAR system design, the PRF (Positioning Frequency Regulator) is typically 1.1-1.2 times the Doppler bandwidth. Limited azimuth sampling samplees the theoretically infinite Doppler spectrum, leading to aliasing of the Doppler spectrum, which ultimately manifests as azimuth ambiguity in the SAR image. For example... Figure 2 As shown in (b), when the Doppler frequency of the echo in this region is in [f d -PRF / 2,f d When the echo Doppler frequency is within [f], this region is called the main imaging region. d -PRF / 2±n·PRF,f d When f = +PRF / 2±n·PRF], the region is called the nth (n≠0, n∈Z) fuzzy region, where f d This is the Doppler center frequency. The azimuth ambiguity signal for a point target in SAR can be written as:

[0046]

[0047] Slant range R corresponding to ambiguous signals at different azimuth times am (η) is:

[0048]

[0049] Where PRT is the reciprocal of the pulse repetition frequency PRF.

[0050] Due to echo delay, the echo signal after one-dimensional range pulse compression exhibits parabolic range migration characteristics. After range migration correction, the migration lines in the main imaging area are completely straightened, while the migration lines in the blurred areas aliased into the main imaging area are tilted. Tilted migration lines, after azimuth compression, will cause range blur and azimuth defocus in the imaging results, such as... Figure 3 As shown.

[0051] Transforming the SAR point target signal to the range Doppler domain, we have:

[0052]

[0053] The distance migration equation R of the Doppler domain rd (f η)for:

[0054]

[0055] Based on the ambiguity characteristics, the distance migration equation R can be... rd (f η Rewritten as:

[0056]

[0057] The nth-order azimuth ambiguity echo signal in the range-Doppler domain can be obtained as follows:

[0058]

[0059] By utilizing the precise expression for the nth-order azimuth blur echo signal, the focusing of the azimuth blur in the image domain can be achieved, such as... Figure 4 As shown.

[0060] Step 2: Based on the range migration correction compensation term and the azimuth phase compensation term, refocus the nth-order azimuth blur on the SAR image;

[0061] The distance migration correction and azimuth phase correction terms are as follows:

[0062]

[0063] Based on equations (8) and (9), it can be further written as follows:

[0064]

[0065] After performing azimuth IFFT on the image, range migration correction compensation and azimuth phase compensation are performed, and finally azimuth FFT can be used to obtain the blurred focused image after refocusing.

[0066] Step 3: Calculate the image entropy difference between the SAR image and the nth-order azimuth-blurred refocused image by dividing them into blocks, and use the CFAR algorithm to complete the detection of azimuth-blurred regions.

[0067] Using the precise expression for the nth-order azimuth-ambiguous echo signal, a derived image can be generated from the original SLC image, including the focused imaging results for each blurred region to be suppressed. To accurately locate the azimuth-ambiguous distribution area, we need to find it through focusing accuracy. Generally, the focusing quality of a SAR image can be measured by image entropy.

[0068]

[0069] Where I(x,y) is the gray value of the image, and M and N are the total number of sample points in the azimuth and range directions, respectively. According to the definition of entropy, the smaller the image entropy, the better the image focusing effect.

[0070] The azimuth-blurred focusing imaging result includes not only the azimuth-blurred target but also the defocused results of the main imaging region and other blurred regions of different orders. The backscattering intensity in these regions is weaker than that in the blurred focusing region. The problem of target detection in the blurred focusing region against the background of the defocused main imaging region can be transformed into the problem of entropy detection after image segmentation. The detection of the target in the main blurred region can be achieved by using the CFAR detection method on the change in entropy difference before and after refocusing.

[0071] ΔE=E refoused -E original (17)

[0072] Among them, E refoused E represents the image entropy after refocusing. original This represents the initial SAR image entropy.

[0073] This algorithm achieves detection by comparing the entropy difference between the blurred focused image and the initial SAR image. Adaptive target detection is achieved using an entropy threshold obtained by modeling the image entropy value within the target window with the false alarm rate (PFA) and background clutter image entropy. The background clutter is assumed to be a Gaussian distribution Φ with mean μ and variance σ, respectively. The threshold T can be expressed as:

[0074]

[0075] The essence of azimuth-blurred target detection is to select a suitable threshold T such that the image meets the condition of a false alarm rate P. a Under the premise of dividing into two distinguishable regions:

[0076]

[0077] Step 4: Using the Wiener filtering method, generate a low-blurred, low-resolution image; combine the results of the orientation-blurred region detection, and obtain a low-blurred, high-resolution image by replacing the detected sub-blocks, thus completing the orientation-blurred suppression.

[0078] Since ambiguity energy is often concentrated at both ends of the Doppler spectrum, the low correlation of ambiguity spectra can be utilized to reduce azimuth ambiguity energy through azimuth spectrum weighting, or regions with higher ambiguity energy can be screened by the difference in energy distribution before and after spectral weighting. However, this method also alters the spectral shape of the main target, interfering with the imaging results of the true target. The most direct impact is a significant decrease in the resolution of the blurred areas. Therefore, an azimuth ambiguity suppression algorithm based on low-ambiguity image replacement is proposed. By using the azimuth ambiguity detection results and the spectrally truncated low-ambiguity SAR image, and replacing the blurred regions, the algorithm achieves the transformation from a high-resolution, high-ambiguity SAR image to a high-resolution, low-ambiguity, high-quality SAR image.

[0079] SAR echo energy can be expressed in the range-Doppler domain as:

[0080] s rd (f η ,τ)=Γ0G0(f η ,τ)+Γ + G + (f η ,τ)+Γ_G_(f η ,τ) (20)

[0081] Where Γ represents the backscattering coefficient after Fourier transform.

[0082] Simultaneously, the echo signals without ambiguity and those affected by additive white-heated noise can be defined as follows:

[0083] s0(f η ,τ)=Γ0G0(f η ,τ) (21)

[0084] X(f η ,τ)=Γ0G0(f η ,τ)+Γ + G + (f η ,τ)+Γ_G_(f η ,τ)+T(f η ) (twenty two)

[0085] The Wiener filter, by using orientation samples of the image, can remove orientation blur and minimize the mean square error between the "true" unblurred signal and the filtered signal. The power spectral densities of the main image target and the blurred target are as follows:

[0086]

[0087] Where E[·] represents the mean and σ represents the power spectral density of the backscattering coefficient, and it is assumed that it has statistical stationary characteristics within the range of the spatial filter.

[0088] Based on the assumed conditions, the expression for the Wiener filter is:

[0089]

[0090] Considering the above expression, assuming that both the echo data and the unambiguous data have zero mean and are uncorrelated, we can obtain from equation (24):

[0091]

[0092] Where σ t This represents the power density of incandescent noise.

[0093] Considering σ = σ + ≈σ -Equation (25) can be further rewritten as:

[0094]

[0095] After filtering (26), a low-blurred, low-resolution image with azimuth blur energy removed is obtained. The azimuth blur region is then replaced using the results obtained from azimuth blur detection. Thanks to the Wiener filtering method and the idea of ​​local image processing, azimuth blur suppression with almost no resolution loss is finally achieved.

[0096] Simulation Experiment: The simulation parameters of the azimuth blur detection and suppression method for spaceborne SAR scene matching curve imaging are shown in Table 1.

[0097] Table 1. Simulation Parameter List for Azimuth Blur Detection and Suppression Method of Spaceborne SAR Scene Matching Curve Imaging

[0098]

[0099] To verify the advantages of the azimuth ambiguity detection and suppression method for spaceborne SAR scene matching curve imaging in solving the azimuth ambiguity problem in spaceborne SAR non-track scene matching curve imaging mode, the azimuth ambiguity suppression method described in this invention was used to design point target azimuth ambiguity suppression under the parameters in Table 1.

[0100] exist Figure 5 The paper presents the focusing imaging results of the blurred area after range migration correction compensation and azimuth phase compensation using the method proposed in this invention, wherein... Figure 5 (a) and (b) show that after distance migration correction compensation, the distance migration line in the ±1st order fuzzy region is corrected to a straight line; Figure 5 (c) and (d) are the focused images of the ±1st order blur region after azimuth phase compensation; clearly, the method described in this invention has a good focusing effect on azimuth-blurred echoes. Figure 5 (e) and (f) respectively give Figure 5 The azimuth peak sidelobe ratio imaging performance indexes of (c) and (d) show that the peak amplitude reflects good focusing effect. Figure 6 (a) and (b) show the focusing results of the main target image without processing by the method described in this invention and the azimuth peak sidelobe ratio. It can be seen that the unprocessed front azimuth peak sidelobe is -37.35dB. Figure 6 Tables (c) and (d) show the focusing results of the main target image processed by the method described in this invention and the azimuth peak sidelobe ratio. It can be seen that the unprocessed front azimuth peak sidelobe is -54.78dB, and the processed azimuth peak sidelobe is reduced by 17.43dB, which significantly reduces the impact of azimuth blur on SAR image quality, while preserving the resolution of the original image. The suppression effect is shown in Table 2.

[0101] Table 2. The effect of the orientation ambiguity suppression method proposed in this paper.

[0102] Azimuth peak sidelobe ratio Azimuth resolution Before inhibition -37.35dB 1.51m After inhibition -54.78dB 1.51m

[0103] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting and suppressing azimuth ambiguities in space-borne SAR scene-matched curve imaging, characterized in that, The method comprises the following steps: Step one, modeling on azimuth ambiguity, respectively giving azimuth ambiguity echo analytical expression, range migration correction compensation item and azimuth phase compensation item; Step two, according to the range migration correction compensation item and the azimuth phase compensation item, refocusing n-order azimuth ambiguity based on the SAR image; the range migration correction compensation item and the azimuth phase compensation item are respectively: ； wherein, is a reference slant range, is a fuzzy zone reference slant range, is an azimuth frequency, is a range migration correction compensation term, is an azimuth phase compensation term; Step three, block computing image entropy difference of SAR image and n The azimuth ambiguity area is detected by using CFAR algorithm. The image entropy is: ； wherein, is the gray value of the image, is the number of occurrences of different gray values of the image, M and N is the total number of sample points in the image orientation and distance directions; By using the CFAR detection method to change the entropy difference before and after refocusing, the detection of the main ambiguous area target is realized: ； wherein, denotes the entropy of the refocused image, denotes the entropy of the initial SAR image, denotes the entropy difference before and after processing; Step four, filtering the SAR image by using a Wiener filter to generate a low-ambiguous and low-resolution image; combining the azimuth ambiguity area detection result, a low-ambiguous and high-resolution image is obtained through detection sub-block replacement, and the ambiguity suppression is completed; the Wiener filter is: ； wherein is an azimuthal frequency domain filter, is an azimuthal frequency, and are the main image target and the blur target backscatter coefficients, respectively, is the white noise power density, is the antenna gain.

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