A method for azimuth ambiguity suppression based on fuzzy area imaging and detection

Abstract

The present invention proposes an azimuth ambiguity suppression method based on fuzzy area imaging and detection, firstly, the strong target position of the original image is detected, and the strong target of the original image is suppressed; then, the suppressed data is subjected to fuzzy area focusing imaging, and the fuzzy area target is detected; after suppressing and eliminating the detected fuzzy area target, the real image is focused back through the inverse process, so as to obtain the SAR image after azimuth ambiguity suppression. The present invention retains the quality of the real target image to the greatest extent while suppressing the azimuth ambiguity, truly achieving targeted suppression of azimuth ambiguity, and does not require any modification to the formation process of the SAR image and the design of the SAR system, and does not require any artificial adjustment of any parameters, and can realize fully automatic engineering processing.

Description

Technical Field

[0001] The invention relates to a synthetic aperture radar (SAR) signal processing technology, and in particular to an azimuth ambiguity suppression method based on fuzzy zone imaging and detection. Background Art

[0002] Azimuth ambiguity is an inherent feature of SAR images and one of the main indicators for defining SAR image quality. Since the azimuth beam pattern extends beyond the main lobe, the azimuth signal is not a band-limited signal, which means that the azimuth signal components will overlap with each other in a blurred form, and eventually present an unfocused blurred result on the image, greatly affecting the image quality. Although most SAR systems are carefully designed to reduce azimuth ambiguity as much as possible, it is still difficult to completely eliminate the azimuth ambiguity of targets with very high scattering intensity through system design. In addition, there are also cases where the system parameter design is not reasonable, so that the azimuth ambiguity area cannot be well suppressed, thus affecting the image quality. Therefore, without changing the system design, there is an urgent need and great application value to suppress azimuth ambiguity through data processing.

[0003] The literature [AMGuarnieri, "Adaptive removal of azimuth ambiguities in SAR images," IEEE Trans. Geosci. Remote Sens. vol. 43, no. 3, pp. 625-633, Mar. 2005.] proposed an azimuth ambiguity suppression method based on spectrum filtering and extrapolation. This method reduces azimuth spectrum aliasing through spectrum filtering and suppresses azimuth ambiguity. However, this method has limited ambiguity suppression effect and reduces the azimuth resolution of the image. It suppresses ambiguity by sacrificing azimuth bandwidth, which has a greater impact on the original image. The literature [Y.Wu,et al.,“Suppression ofazimuth ambiguities in spaceborne SAR images using spectralselection and extrapolation,”IEEE Trans.Geosci.Remote Sens.vol.56,no.10,pp.6134-6147,Oct.2008.] proposed a method to suppress azimuth ambiguity using spectral selection and extrapolation. This method mainly calculates a simulated false image from the real target where the ambiguity originates and then suppresses and removes it. This method requires an accurate understanding of the origin of the ambiguity and requires more prior knowledge, which is relatively limited in practical applications.

[0004] Patent [Han Bing, Zhang Yongjun, et al., 2011. SAR azimuth ambiguity suppression method based on antenna main lobe dominant strength constraint, patent application number: 201110245613.8] proposes a strength constraint algorithm based on antenna main lobe dominance. This method requires the existence of an azimuth antenna main lobe dominant area, and the suppression effect is sensitive to the width of the dominant area and the dominance of the main signal energy. Patent [Zhao Hanxi, Wang Wei, Wang Zhuoqun, et al. "A method for suppressing near-coastal clutter and its azimuth ambiguity in SAR images", CN110261855A.2019.] proposes a method for suppressing near-coastal clutter and its azimuth ambiguity based on spatial frequency domain processing. This method requires obtaining N images of the same area at different times for spatial domain processing, which has great limitations. Patent [Li Wenchao, Li Jieyang, Yang Jianyu, et al. "SAR image azimuth blur suppression method", CN104182942A.2014.] proposed an azimuth blur suppression method based on image segmentation and azimuth windowing. The core of this method is still to achieve azimuth spectrum smoothing filtering through windowing. Even if it is combined with edge detection and image segmentation, its suppression effect is still affected by the shortcomings of spectrum filtering, the resolution is reduced and it cannot cope with severe azimuth blur. Patent [Chen Jie, Wang Kai, Ma Baobo, et al. "A method for suppressing azimuth blur in strip mode of spaceborne synthetic aperture radar", CN103605119A.2014.] proposed an azimuth blur suppression method for the sea-land boundary area of ​​strip mode. This method needs to calculate the fuzzy target position and divide it into blocks, and finally use the method of patching adjacent areas to suppress the fuzzy target. The method has great limitations and is not applicable in the case of large-scale severe azimuth blur.

[0005] As can be seen from the above situation, the existing SAR azimuth ambiguity suppression methods are mainly based on the idea of ​​selecting a spectrum with less severe ambiguity for imaging, or based on the idea of ​​sparse reconstruction. However, in reality, due to the ductility of the SAR antenna pattern, it is very likely that there will be a relatively serious ambiguity spectrum in a fairly wide frequency band in the main area, and it is difficult to find an area with less severe ambiguity, so the application is relatively limited. In addition, even if the spectrum mixed in from some fuzzy areas is suppressed by spectral filtering and other methods, it will also affect the main area spectrum, reduce the bandwidth and thus reduce the azimuth resolution, thereby affecting the final image effect. The sparse reconstruction method is very inefficient on the one hand, and on the other hand, it will set all weak targets in the scene to zero, which will be quite different from traditional SAR image products and may even affect some applications. Summary of the invention

[0006] The purpose of the present invention is to propose an azimuth ambiguity suppression method based on fuzzy area imaging and detection, so as to well preserve the quality of the real target image while suppressing the azimuth ambiguity, and truly achieve targeted suppression of the azimuth ambiguity.

[0007] The technical solution to achieve the purpose of the present invention is: a method for suppressing azimuth ambiguity based on fuzzy area imaging and detection, comprising the following steps:

[0008] Step 1: Main area image energy suppression: For the SAR single-view complex image with azimuth ambiguity in the focused imaging, the amplitudes of all pixels are set to a uniform value to generate a SAR image with normalized amplitude and preserved phase;

[0009] Step 2: Further suppressing strong targets in the main area image: setting a threshold to detect strong point targets in the SAR single-view complex image, finding the position corresponding to the strong point target in the SAR image obtained in step 1 that only retains the phase, and further suppressing the amplitude of the position to obtain a pure phase SAR image after suppressing the strong point target;

[0010] Step 3, k-th order azimuth fuzzy area focused imaging: perform azimuth Fourier transform on the original SAR single-view complex image, calculate the range unit migration of the k-th order azimuth fuzzy signal, perform range migration correction, azimuth phase compensation and azimuth inverse Fourier transform on the range Doppler domain data, and obtain the fuzzy area focused imaging result of the SAR single-view complex image;

[0011] Step 4, focusing imaging of the k-th order azimuth fuzzy area of ​​the pure phase image: for the pure phase SAR image after the strong point target is suppressed obtained in step 2, the same focusing imaging processing as step 3 is performed to obtain the fuzzy area focusing imaging result of the pure phase SAR image;

[0012] Step 5, k-th order blurred target detection and suppression: for the blurred area focusing imaging result of the pure phase SAR image obtained in step 4, the blurred target is detected by setting a threshold, the position corresponding to the blurred target is found in the blurred area focusing imaging result of the SAR single-view complex image obtained in step 3, the target at the position is suppressed, and the suppressed data is obtained;

[0013] Step 6, refocusing the main area image: perform the inverse operation of step 3 on the data suppressed in step 5, i.e., perform azimuth FFT, azimuth phase inverse compensation, range migration inverse correction, and azimuth IFFT to obtain an image with the k-th order blur removed;

[0014] Step 7: Loop to determine the final deblurred image result: Repeat steps 3 to 6 until all k-order blurs are removed to obtain the final deblurred image result.

[0015] Furthermore, in step 3, in the range Doppler domain data, the range unit migration of the k-th order azimuth ambiguity signal is expressed as follows:

[0016]

[0017] Among them, R(k,f η ) represents the distance unit migration of the k-th order azimuth ambiguity signal, f η represents the Doppler frequency, f PRF represents the system pulse repetition frequency, R0 represents the short distance, λ represents the wavelength, V r represents the equivalent velocity, cosθ k It is expressed as follows:

[0018]

[0019] Using formula (1.1) to perform range migration correction on range Doppler domain data can straighten the range migration of k-order ambiguous signals.

[0020] Furthermore, in step 3, the specific formula for azimuth phase compensation is as follows:

[0021]

[0022] where Ψ(k,f η ) represents the value of azimuth phase compensation when imaging the k-th order azimuth ambiguity region.

[0023] Furthermore, in step 5, the position corresponding to the blurred target is found in the blurred area focusing imaging result of the SAR single-view complex image, and the target at the position is suppressed by using a notch method to obtain suppressed data.

[0024] A system for suppressing azimuth ambiguity based on fuzzy area imaging and detection realizes azimuth ambiguity suppression based on fuzzy area imaging and detection based on the method for suppressing azimuth ambiguity based on fuzzy area imaging and detection.

[0025] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the azimuth ambiguity suppression method based on fuzzy area imaging and detection is implemented based on the azimuth ambiguity suppression method based on fuzzy area imaging and detection.

[0026] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements azimuth ambiguity suppression based on fuzzy area imaging and detection based on the azimuth ambiguity suppression method based on fuzzy area imaging and detection.

[0027] Compared with the prior art, the present invention has the following significant advantages: 1) it can suppress azimuth blur in a targeted manner without affecting the resolution quality of the original image; 2) it does not require prior knowledge such as the location of the blur area; 3) it can automate the entire process, facilitate engineering implementation, and is highly efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is the original SAR image with severe azimuth ambiguity.

[0029] Figure 2 This is the SAR image after azimuth ambiguity suppression according to the present invention.

[0030] Figure 3 It is a flow chart of the method for azimuth ambiguity suppression of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0032] The present invention proposes an azimuth blur suppression method based on blur area imaging and detection, which is applicable to various imaging modes such as strip, scanning, and beamforming. The basic idea is to focus on the images of the azimuth blur areas of the SAR image through a reversible signal processing method, detect the strong targets in the blur area, and suppress and eliminate them in a targeted manner, and then obtain the real target image of the main area after the blur is suppressed through the inverse operation. In blur area imaging, the present invention first detects the position of the strong target in the original image and suppresses the strong targets in the original image; then performs blur area focusing imaging on the suppressed data and detects the blur area targets; after suppressing and eliminating the detected blur area targets, the real image is focused back through the inverse process, thereby obtaining the SAR image after azimuth blur suppression. The specific processing flow is as follows:

[0033] Step 1: Main area image energy suppression.

[0034] For the SAR single-view complex image with azimuth ambiguity in the focused imaging, strong targets are suppressed. Here, the amplitude information of the SAR image is discarded, that is, the amplitude of all pixels is set to a uniform value to suppress it, and a SAR image with normalized amplitude and retained phase is generated.

[0035] Step 2: Further suppress strong targets in the main area image.

[0036] A threshold is set to detect strong point targets in the original image, the positions of these strong point targets are stored, and the corresponding positions are found in the phase-only SAR image obtained in step 1. The amplitude of this position is further suppressed to obtain a pure phase SAR image after strong point target suppression, which is recorded as S1.

[0037] Step 3: Focus imaging of the k-th order azimuthal fuzzy area.

[0038] Perform focused imaging of the k-th order azimuth ambiguity area, where k is usually ±1. In order to obtain the focused image of the k-th order azimuth ambiguity area, first perform azimuth Fourier transform (FFT) on the original SAR single-view complex image to obtain range Doppler domain data. In the range Doppler domain data, the range unit migration of the k-th order azimuth ambiguity signal is as follows:

[0039]

[0040] Among them, R(k,f η ) represents the distance unit migration of the k-th order azimuth ambiguity signal, f η represents the Doppler frequency, f PRF represents the system pulse repetition frequency, R0 represents the short distance, λ represents the wavelength, V r represents the equivalent velocity, cosθ k It is expressed as follows:

[0041]

[0042] Use equation (1.1) to perform range migration correction on the range Doppler domain data to straighten the range migration of the k-order blurred signal. Then perform azimuth phase compensation according to equation (1.3) and perform azimuth inverse Fourier transform (IFFT) to obtain the image after k-order azimuth blurred focusing, recorded as S(k).

[0043]

[0044] Step 4: Focus imaging of the k-th order azimuthal fuzzy region of the pure phase image.

[0045] The same focusing imaging processing as step 3 is performed on the pure phase image S1 processed by steps 1 and 2 to obtain the focusing imaging result of the fuzzy area of ​​the pure phase image, which is recorded as S1(k).

[0046] Step 5: k-th order blurred target detection and suppression.

[0047] For the S1(k) image obtained in step 4, the blurred target is detected by setting a threshold value and recording the specific position. The corresponding position is found in the focus blur map S(k) generated by the original SAR image, and the target at this position is greatly suppressed, such as by using a notch method, and the suppressed data is recorded as S′(k).

[0048] Step 6: Refocus the main area image.

[0049] Perform the inverse operation of step 3 on S′(k), that is, perform azimuth FFT, azimuth phase inverse compensation based on formula (1.3), range migration inverse correction based on formula (1.1), and azimuth IFFT, so as to obtain the image S″(k) with the k-th order blur removed.

[0050] Step 7: Repeat steps 3 to 6 until all k-order blurs are removed to obtain the final deblurred image result.

[0051] In addition, step 2 can also use other methods such as deep neural networks to detect strong point targets and perform amplitude suppression; step 3 can also use other imaging methods such as envelope alignment and self-focusing to perform focused imaging in the blurred area; step 5 can also use other methods such as deep neural networks to detect targets in the blurred area and suppress them.

[0052] The present invention also proposes an azimuth ambiguity suppression system based on blur area imaging and detection, and implements azimuth ambiguity suppression based on blur area imaging and detection based on the azimuth ambiguity suppression method based on blur area imaging and detection.

[0053] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the azimuth ambiguity suppression method based on fuzzy area imaging and detection is implemented based on the azimuth ambiguity suppression method based on fuzzy area imaging and detection.

[0054] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements azimuth ambiguity suppression based on fuzzy area imaging and detection based on the azimuth ambiguity suppression method based on fuzzy area imaging and detection.

[0055] In summary, the present invention obtains images of azimuth blur areas of SAR images by focusing, detects targets in blur areas and suppresses and eliminates them in a targeted manner, and then obtains the real target image of the main area after suppressing blur through inverse operations, thereby avoiding the shortcomings of existing methods, and retaining the quality of the real target image to the greatest extent while suppressing azimuth blur, thereby truly achieving targeted suppression of azimuth blur. In addition, the method of the present invention does not require any modification to the formation process of SAR images and the design of SAR systems. In the entire process, no parameters need to be manually adjusted, and fully automatic engineering processing can be achieved.

[0056] In order to illustrate the effectiveness and superiority of the solution of the present invention, a SAR satellite image with severe azimuth ambiguity was selected for experiment. Figure 1-2 As shown, it can be seen that the azimuth ambiguity suppression method of the present invention is effective.

[0057] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0058] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for suppressing azimuth ambiguity based on fuzzy area imaging and detection, characterized in that: The steps include: Step 1: Main area image energy suppression: For the SAR single-view complex image with azimuth ambiguity in the focused imaging, the amplitudes of all pixels are set to a uniform value to generate a SAR image with normalized amplitude and preserved phase; Step 2: Further suppressing strong targets in the main area image: setting a threshold to detect strong point targets in the SAR single-view complex image, finding the position corresponding to the strong point target in the SAR image obtained in step 1 that only retains the phase, and further suppressing the amplitude of the position to obtain a pure phase SAR image after suppressing the strong point target; Step 3, k-th order azimuth fuzzy area focused imaging: perform azimuth Fourier transform on the original SAR single-view complex image, calculate the range unit migration of the k-th order azimuth fuzzy signal, perform range migration correction, azimuth phase compensation and azimuth inverse Fourier transform on the range Doppler domain data, and obtain the fuzzy area focused imaging result of the SAR single-view complex image; Step 4, focusing imaging of the k-th order azimuth fuzzy area of ​​the pure phase image: for the pure phase SAR image after the strong point target is suppressed obtained in step 2, the same focusing imaging processing as step 3 is performed to obtain the fuzzy area focusing imaging result of the pure phase SAR image; Step 5, k-th order blurred target detection and suppression: for the blurred area focusing imaging result of the pure phase SAR image obtained in step 4, the blurred target is detected by setting a threshold, the position corresponding to the blurred target is found in the blurred area focusing imaging result of the SAR single-view complex image obtained in step 3, the target at the position is suppressed, and the suppressed data is obtained; Step 6, refocusing the main area image: perform the inverse operation of step 3 on the data suppressed in step 5, i.e., perform azimuth FFT, azimuth phase inverse compensation, range migration inverse correction, and azimuth IFFT to obtain an image with the k-th order blur removed; Step 7: Loop to determine the final deblurred image result: Repeat steps 3 to 6 until all k-order blurs are removed to obtain the final deblurred image result.

2. The method for suppressing azimuth ambiguity based on fuzzy area imaging and detection according to claim 1, characterized in that: In step 3, in the range Doppler domain data, the range unit migration of the k-th order azimuth ambiguity signal is expressed as follows: Among them, R(k,f η ) represents the distance unit migration of the k-th order azimuth ambiguity signal, f η represents the Doppler frequency, f PRF represents the system pulse repetition frequency, R0 represents the short distance, λ represents the wavelength, V r represents the equivalent velocity, cosθ k It is expressed as follows: Using formula (1.1) to perform range migration correction on range Doppler domain data can straighten the range migration of k-order ambiguous signals.

3. The method for suppressing azimuth ambiguity based on fuzzy area imaging and detection according to claim 2, characterized in that: In step 3, the specific formula for azimuth phase compensation is as follows: where Ψ(k,f η ) represents the value of azimuth phase compensation when imaging the k-th order azimuth ambiguity region.

4. The method for suppressing azimuth ambiguity based on fuzzy area imaging and detection according to claim 1, characterized in that: In step 5, the position corresponding to the blurred target is found in the blurred area focusing imaging result of the SAR single-view complex image, and the target at the position is suppressed by using the notch method to obtain the suppressed data.

5. An azimuth ambiguity suppression system based on fuzzy area imaging and detection, characterized in that: Based on the azimuth ambiguity suppression method based on blur area imaging and detection described in any one of claims 1 to 4, azimuth ambiguity suppression based on blur area imaging and detection is achieved.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the azimuth ambiguity suppression method based on blur area imaging and detection according to any one of claims 1 to 4 is used to implement azimuth ambiguity suppression based on blur area imaging and detection.

7. A computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the azimuth ambiguity suppression method based on blur area imaging and detection according to any one of claims 1 to 4 is used to implement azimuth ambiguity suppression based on blur area imaging and detection.

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