An interference artifact separation method based on amplitude image spatial domain notch
By using the amplitude image spatial domain notch method, the energy concentration and symmetry characteristics of the interference signal are utilized to achieve effective separation and suppression of interference artifacts. This solves the problems of high computational complexity and insufficient applicability in existing technologies, and improves SAR image quality and target detection capabilities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHWESTERN POLYTECHNICAL UNIV
- Filing Date
- 2026-02-09
- Publication Date
- 2026-06-23
AI Technical Summary
Existing SAR interference suppression methods are difficult to apply directly to post-image GRD images. Furthermore, existing methods have high computational complexity or are highly dependent on training data, resulting in poor interference artifact suppression performance, which affects image quality and subsequent application performance.
An amplitude image spatial domain notch filtering method is adopted, which extracts the spectrum through two-dimensional discrete Fourier transform. By utilizing the high energy concentration and symmetry characteristics of the interference signal and combining median absolute deviation detection, an interference mask is constructed for suppression, thereby realizing the identification and suppression of interference spectrum components.
It effectively separates interference artifacts, recovers target information, improves image quality and subsequent target detection performance without requiring phase information or prior parameters, and has high computational efficiency and strong applicability.
Smart Images
Figure CN122265045A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of SAR image processing technology, specifically relating to an interference artifact separation method based on amplitude image spatial domain notch filtering. Background Technology
[0002] Synthetic Aperture Radar (SAR) imaging plays a crucial role in remote sensing, target identification, and resource exploration. However, due to external radio frequency interference, significant interference artifacts, such as bright lines and bright spots, often appear in measured SAR amplitude images. These artifacts severely degrade image quality, causing texture distortion, blurred edge information, and adversely affecting subsequent applications such as target detection, classification, and quantitative analysis. Therefore, effectively suppressing interference artifacts and recovering true imaging information from the acquired amplitude images has become a key technical challenge for improving SAR image quality and practical performance.
[0003] Existing SAR interference suppression methods primarily operate in the echo signal domain or complex image domain, typically relying on complete phase information and imaging parameters. Interference suppression is achieved through frequency domain filtering, subspace decomposition, or sparse reconstruction. These methods have high data requirements and are difficult to directly apply to widely used Ground Range Detected (GRD) image products in engineering. Furthermore, while image domain methods based on low-rank decomposition or deep learning have some effectiveness, their high computational complexity and strong dependence on training data limit their versatility in practical applications with only single-image amplitude values. Summary of the Invention
[0004] To overcome the shortcomings of existing technologies, this invention provides an interference artifact separation method based on spatial domain notch filtering of amplitude images. Using an amplitude image as input, a two-dimensional discrete Fourier transform (2D FFT) is performed to obtain the spatial domain spectrum. The interference signal exhibits high energy concentration, strong directionality, and a significantly symmetrical structure in the spectrum, which differs from the smooth low-frequency distribution of the sea surface background and the dispersed, irregularly symmetrical spectrum of real targets such as ships. Then, interference identification and adaptive notch filtering are performed in the spatial domain to suppress the interference spectral components. Subsequently, an inverse transform is performed on the processed spectrum to reconstruct the interference-suppressed amplitude image. This invention fully exploits the structural characteristics of amplitude images in the spatial domain, utilizing the high energy concentration, strong directionality, and significantly symmetrical distribution of the interference signal in the spectrum, and the difference between this and the smooth low-frequency distribution of the sea surface background and the dispersed, irregularly symmetrical distribution of the spectrum of targets such as ships. This allows for effective identification and suppression of interference spectral components without requiring complex data or prior interference parameters.
[0005] The technical solution adopted by this invention to solve its technical problem is as follows: Step 1: For an image of size... Original amplitude image containing interference Perform a two-dimensional discrete Fourier transform to obtain the spatial domain spectrum. ,in, , These represent the number of sampling points in the distance and orientation dimensions of the GRD image, respectively. , This represents the sampling units for the azimuth and distance dimensions; The two-dimensional discrete Fourier transform can be expressed as:
[0006] in, These are the spatial frequencies in the range and azimuth directions, respectively. Step 2: Employ a global threshold detection criterion based on median absolute deviation (MAD); Step 2-1: Calculate the spectral amplitude Logarithmic form:
[0007] Let the median of all spectral amplitudes be 1. Its MAD definition is:
[0008] in, This indicates the operation to find the median; The global detection threshold is defined as follows:
[0009] in, It is a constant; when At that time, the point was identified as an interference frequency component; Step 2-2: Construct an interference mask :
[0010] Step 3: Spatial domain spectrum Multiplying the spectrum by the interference mask yields the spatial domain spectrum after interference suppression. Then, an inverse two-dimensional discrete Fourier transform is performed to obtain the amplitude image after interference artifact separation. .
[0011] Preferably, the .
[0012] An electronic device includes a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to enable the electronic device to perform the above-described interference artifact separation method.
[0013] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described interference artifact separation method.
[0014] A chip includes a processor for retrieving and running a computer program from a memory, causing a device on which the chip is mounted to perform the aforementioned interference artifact separation method.
[0015] A computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the above-described interference artifact separation method.
[0016] The beneficial effects of this invention are as follows: This invention innovatively proposes a method for separating interference artifacts based on amplitude image spatial domain notch filtering. It achieves effective separation of interference artifacts without requiring phase or frequency domain information. This invention fully utilizes the energy concentration and strong symmetry of interference signals in the spatial domain spectrum to suppress interference bright bands and stripe artifacts while recovering the intensity and structural information of targets such as ships obscured by interference artifacts. By performing interference suppression at the post-imaging product level, this method has advantages such as simple implementation, high computational efficiency, and strong applicability, providing a more reliable data foundation for subsequent target detection and recognition applications, and possessing significant engineering application value. Attached Figure Description
[0017] Figure 1 This is a schematic diagram illustrating the implementation process of the method of the present invention; Figure 2 These are the original SAR amplitude image with interference and the spatial domain spectrum in the embodiments of the present invention. Figure 2 (a) shows two SAR amplitude images with interference. Figure 2 (b) is Figure 2 (a) Spatial domain spectrum corresponding to the original image; Figure 3 This is the interference mask generated in the embodiments of the present invention. Figure 3 (a) and Figure 3 (b) is Figure 2 (b) Interference mask extracted from the two spatial domain spectra after threshold detection; Figure 4 This is an image processed by the interference suppression method of this invention in an embodiment of the invention. Figure 4 (a) and Figure 4 (b) is Figure 2 (a) shows the amplitude image results of the two original images containing interference after processing by the method proposed in this invention; Detailed Implementation The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0018] To address the issue that existing SAR interference suppression techniques often rely on complex echo data or intermediate results during imaging, making it difficult to directly apply to post-imaging GRD images, this invention proposes an interference artifact separation method based on amplitude image spatial domain notch filtering. This invention fully leverages the structural characteristics of amplitude images in the spatial domain, utilizing the high energy concentration, strong directionality, and significantly symmetrical distribution of interference signals in the spectrum. This contrasts with the low-frequency smooth distribution of the sea surface background and the discrete, irregular, and symmetrical distribution of the spectrum of targets such as ships. This allows for effective identification and suppression of interference spectral components without requiring complex data or prior interference parameters. By performing interference artifact separation directly at the amplitude image level, this invention maximizes the preservation of target structure and edge information while maintaining interference suppression effectiveness, significantly improving GRD image quality and the performance of subsequent target detection and recognition applications.
[0019] The basic idea behind this invention is to directly use an amplitude image as input and perform a two-dimensional discrete Fourier transform (2D FFT) on it to obtain the spatial domain spectrum. The interference signal exhibits high energy concentration, strong directionality, and a significantly symmetrical structure in the spectrum, which differs from the smooth low-frequency distribution of the sea surface background and the dispersed, irregular, and symmetrical spectrum of real targets such as ships. Then, interference identification and adaptive notch filtering are performed in the spatial domain to suppress the interference spectral components. Finally, an inverse transform is performed on the processed spectrum to reconstruct the interference-suppressed amplitude image.
[0020] like Figure 1 As shown, in order to achieve the above objectives, the technical solution of the present invention includes the following steps: Step 1: First, for an image of size... Original amplitude image containing interference Perform a two-dimensional discrete Fourier transform to obtain the spatial domain spectrum. ,in, , These represent the number of sampling points in the distance and orientation dimensions of the SLC image, respectively. , This represents the sampling unit for the azimuth and distance dimensions.
[0021] The two-dimensional discrete Fourier transform can be expressed as:
[0022] in, These are the spatial frequencies in the range and azimuth directions, respectively.
[0023] Step 2: This invention employs a global threshold detection criterion based on median absolute deviation (MAD). First, the logarithmic form of the spectral amplitude is calculated:
[0024] In the logarithmic domain, the energy differences between different frequency bands are smoothed, which helps to highlight anomalously high-energy components. Let the median of all spectral amplitudes be... Its MAD definition is:
[0025] The global detection threshold can then be defined as:
[0026] in, It is a constant. .when At that time, the point was identified as an interference frequency component.
[0027] Then construct an interference mask. :
[0028] Step 3: Spatial domain spectrum Multiplying the spectrum by the interference mask yields the spatial domain spectrum after interference suppression. Then, an inverse two-dimensional discrete Fourier transform (2D IFFT) is performed to obtain the amplitude image after interference artifact separation. .
[0029] Example: Step 1: First, for an image of size... Original amplitude image containing interference Perform a two-dimensional discrete Fourier transform to obtain the spatial domain spectrum. .
[0030] Step 2: This invention employs a global threshold detection criterion based on median absolute deviation. First, the logarithmic form of the spectral amplitude is calculated:
[0031] Then calculate The median and median absolute deviation, in the example, the image median are respectively , The median absolute deviations are respectively , The global detection threshold is then: ,
[0032] when At that point, the frequency component was identified as interference. Then, an interference mask was constructed. .
[0033] Step 3: Spatial domain spectrum Multiplying the spectrum by the interference mask yields the spatial domain spectrum after interference suppression. Then, an inverse two-dimensional discrete Fourier transform (2D IFFT) is performed to obtain the amplitude image after interference artifact separation. After processing, the ship target information in the original image was effectively enhanced.
[0034] Figure 2 These are the original SAR amplitude image with interference and the spatial domain spectrum in the embodiments of the present invention. Figure 2 (a) shows two SAR amplitude images with interference. Figure 2 (b) is Figure 2 (a) Spatial domain spectrum corresponding to the original image; Figure 3 This is the interference mask generated in the embodiments of the present invention. Figure 3 (a) and Figure 3 (b) is Figure 2 (b) Interference mask extracted from the two spatial domain spectra after threshold detection; Figure 4 This is an image processed by the interference suppression method of this invention in an embodiment of the invention. Figure 4 (a) and Figure 4 (b) is Figure 2 (a) shows the amplitude image results of the two original images containing interference after processing by the method proposed in this invention.
Claims
1. A method for separating interference artifacts based on amplitude image spatial domain notch filtering, characterized in that, Includes the following steps: Step 1: For an image of size... Original amplitude image containing interference Perform a two-dimensional discrete Fourier transform to obtain the spatial domain spectrum. ,in, , These represent the number of sampling points in the distance and orientation dimensions of the GRD image, respectively. , This represents the sampling units for the azimuth and distance dimensions; The two-dimensional discrete Fourier transform can be expressed as: in, These are the spatial frequencies in the range and azimuth directions, respectively. Step 2: Employ a global threshold detection criterion based on median absolute deviation (MAD); Step 2-1: Calculate the spectral amplitude Logarithmic form: Let the median of all spectral amplitudes be 1. Its MAD definition is: in, This indicates the operation to find the median; The global detection threshold is defined as follows: in, It is a constant; when At that time, the point was identified as an interference frequency component; Step 2-2: Construct an interference mask : Step 3: Spatial domain spectrum Multiplying the spectrum by the interference mask yields the spatial domain spectrum after interference suppression. Then, an inverse two-dimensional discrete Fourier transform is performed to obtain the amplitude image after interference artifact separation. .
2. The interference artifact separation method based on amplitude image spatial domain notch filtering according to claim 1, characterized in that, The .
3. An electronic device, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 2.
4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 2.
5. A chip, characterized in that, include: A processor for retrieving and running a computer program from memory, causing a device on which the chip is mounted to perform the method as described in any one of claims 1 to 2.
6. A computer program product, characterized in that, The computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the method as described in any one of claims 1 to 2.