Self-adaptive anti-interference synthetic aperture radar reclamation remote sensing imaging system

Abstract

The invention discloses a self-adaptive anti-interference synthetic aperture radar sea reclamation remote sensing imaging system, and relates to the technical field of synthetic aperture radars. The system is composed of a geometrical optical model, a first acquisition module (regionalized frequency spectrum scanning and active interference positioning), a second acquisition module (wind speed, wave height, wavelength, texture intensity and echo energy) and interference analysis / adaptive control. Through constructing a frequency band offset coefficient, a sea surface disturbance coefficient and a sea surface reflection coefficient and setting a grading threshold value, active / passive interference is cooperatively judged, and a working frequency band, an incident angle, polarization, PRF, a dynamic range, a window and a flight height are adjusted in a closed-loop linkage manner. The system can inhibit stripes and bright bands under complex sea conditions, improves boundary extraction and change detection consistency, adapts to unmanned aerial vehicle airborne, and realizes real-time or quasi-real-time monitoring.

Description

Technical Field

[0001] This invention relates to the field of synthetic aperture radar technology, and more specifically, to an adaptive anti-jamming synthetic aperture radar remote sensing imaging system for reclamation. Background Technology

[0002] With the increasing demand for coastal zone development and utilization, land space management, and land reclamation project supervision in my country, high-precision, comprehensive, and rapidly updated marine monitoring using remote sensing technology has become an important national task. Synthetic Aperture Radar (SAR), as an all-weather, all-time active microwave imaging technology, possesses characteristics such as penetrating clouds and rain, nighttime imaging, and sensitivity to sea surface scattering. It has irreplaceable advantages in scenarios such as coastal zone monitoring, land reclamation supervision, coastline identification, marine environmental detection, and emergency response. Compared to optical and infrared remote sensing, SAR can reliably acquire images even under adverse conditions such as cloudy, rainy, and nighttime conditions, making it one of the most practically valuable remote sensing methods for monitoring land reclamation projects. However, in the complex and ever-changing marine environment, SAR image quality is highly susceptible to multi-source interference. For example, changes in sea surface roughness can lead to unstable backscattering intensity, while wind, waves, and tides can cause local speckle enhancement and edge blurring. Furthermore, the dense network of active radiators near the shore, such as communication base stations and radar equipment, can create frequency shift interference, out-of-band radiation, and co-channel noise, resulting in echo distortion, streaks, bright bands, and other anomalies. These factors limit the effectiveness of SAR in identifying reclamation boundaries, reconstructing structural details, and ensuring consistent change detection, impacting engineering boundary extraction, assessment of reclamation volume changes, and the stability of dynamic monitoring.

[0003] In terms of relevant known technologies, anti-jamming research for synthetic aperture radar (SAR) systems mainly focuses on two directions: first, anti-jamming algorithms at the spectrum and signal processing level, such as spectrum diversity, interference suppression filtering, time-frequency analysis, and coherent accumulation control; second, image-level interpretation enhancement and target extraction methods, such as techniques based on polarization decomposition, active contour models, and interferometric differential (InSAR / DInSAR). These methods have achieved some success in remote sensing image post-processing, but most only address single interference sources or post-hoc image restoration, failing to form a parameter adaptive system for multi-interference environments. Furthermore, existing systems often separate "imaging parameter optimization" from "interference identification," lacking a closed-loop mechanism of real-time perception—parameter feedback—imaging correction. This results in difficulty guaranteeing SAR image quality in complex sea conditions or mixed interference environments, limiting the stable extraction of land reclamation information.

[0004] From the perspective of patent literature, related technologies mainly focus on SAR interpretation and improvements for specific applications, but lack system-level anti-interference and adaptive imaging solutions. For example: Chinese invention patent application CN107507193A proposes an automatic extraction method for land reclamation information from Gaofen-3 synthetic aperture radar (SAR) images. This method utilizes an improved regional range regularization geometric active contour model to extract and vectorize coastlines, ultimately generating a thematic map of land reclamation. While this scheme achieves automatic change detection in high-resolution SAR images, it primarily focuses on image interpretation in the post-processing stage and does not address interference identification or real-time adjustment of imaging parameters.

[0005] Chinese invention patent CN104330798B, authorized by patent number CN104330798B, proposes a method and apparatus for monitoring crop sowing dates based on synthetic aperture radar remote sensing images. It utilizes the polarization decomposition features of fully polarimetric SAR images to invert crop sowing dates. While this scheme demonstrates strong discriminative ability in polarization information extraction, it does not address interference analysis and adaptive adjustment mechanisms in complex maritime environments.

[0006] Chinese invention patent application CN117169885A discloses a method and apparatus for designing variable-view flight paths for airborne synthetic aperture radar, which improves imaging coverage quality by adjusting the side view and flight path position. This method achieves geometric-level imaging optimization, but it is still a pre-planned design and lacks the ability to dynamically adjust based on real-time interference parameters.

[0007] Chinese invention patent application CN109490886A discloses a method for accurately extracting oil spill areas from sea surfaces using polarimetric synthetic aperture radar (SAR). This method combines polarimetric filtering, multi-feature fusion, and a stacked autoencoder (SAE) to achieve precise segmentation of the oil spill area. While this technology improves the classification accuracy of SAR images, it falls under the category of target detection and recognition and does not address solutions to sea surface interference and system adaptive imaging problems.

[0008] Chinese utility model patent CN209460395U discloses a ground-based omnidirectional radar corner reflector for synthetic aperture radar interferometric remote sensing, used to improve calibration accuracy. It improves the geometric consistency of SAR measurements, but as an external auxiliary device, it lacks system anti-interference adjustment capabilities.

[0009] In summary, although existing technologies have made breakthroughs in SAR image post-processing, polarization feature extraction, geometric flight path optimization, and calibration accuracy, they generally suffer from the following shortcomings: lack of a unified quantifiable interference index system, making it impossible to determine the intensity and type of active and natural interference in real time; and lack of an imaging parameter linkage mechanism based on interference identification, such as frequency band, polarization, incident angle, pulse repetition frequency (PRF), and dynamic range adjustment. The lack of closed-loop feedback between imaging parameter adjustment and operational layer interpretation accuracy makes it impossible to quantify the optimization effect; existing methods are mostly posterior corrections or single-stage optimizations, and have not formed a full-link adaptive anti-interference system. Summary of the Invention

[0010] To address the multi-source interference and complex sea conditions affecting remote sensing monitoring of marine reclamation, this invention proposes an adaptive anti-interference synthetic aperture radar (SAR) remote sensing imaging system for marine reclamation. This system establishes a marine radar geometric optics propagation-reflection model, constructs an energy-weighted frequency band offset criterion normalized to the total amount of interference sources to identify active interference, and uses dimensionless sea surface disturbance coefficients and sea surface reflection coefficients to collaboratively determine passive interference. Furthermore, it implements closed-loop adaptive control of the operating frequency band, incident angle, and associated imaging parameters (polarization, pulse repetition frequency, dynamic range, imaging window length, and flight altitude) to solve the problems of imaging distortion, stripe and bright bands, boundary blurring, and unstable detection of reclamation changes caused by complex sea conditions and multi-source interference. The system specifically includes the following modules: T1: Geometric Optical Model Building Module, used to establish a radar wave propagation and reflection model in a marine environment, and to build a mathematical mapping relationship between the radar wave incident angle and the wave wavelength γ. T2: The first acquisition module is used to acquire the spatial location information of active interference sources based on the model described in T1, divide the sea surface into n regions, and use the spectrum scanning unit to scan the radar's current operating frequency band and its adjacent frequency bands in real time, identify the frequency shift caused by active interference sources in each region, and construct a frequency band offset dataset. T3: The second acquisition module is used to construct a sea surface disturbance dataset under different wind speeds v, wave heights h, and wave wavelengths γ, and to construct a sea surface reflection dataset by combining the sea surface texture intensity T and the wave surface slope. T4: Disturbance Analysis and Adaptive Control Module, which is used for: T4-1: Extract the perturbation features of the i-th active interference source in the n-th region Ln from the frequency band offset dataset in module T2, obtain the frequency band center value offset Δpyl, and combine it with the total number of active interference sources Zs, weighted by energy weight K_{n,i} and normalized by Zs to construct the frequency band offset coefficient Bdx, which serves as the core criterion for active anti-interference. T4-2: Perform dimensionless processing on the sea surface disturbance dataset and sea surface reflection dataset in module T3, calculate the sea surface disturbance coefficient Hfx and sea surface reflection coefficient Fxs respectively, and make collaborative decisions with the corresponding thresholds to generate passive anti-interference correction instructions. T4-3: Based on the determination results of Bdx, Hfx and Fxs, the radar operating frequency band and incident angle are corrected adaptively in a closed loop, and the associated imaging parameters are adjusted in a controlled manner to ensure the quality of remote sensing images.

[0011] As a preferred embodiment of the present invention, the geometric optical model construction module is also used to enable the SAR to fly at a reference altitude on the sea surface via a UAV, and can adjust the flight altitude according to the correction instructions generated by the interference analysis and adaptive control module; based on the wave height, incident wave angle and ground reflection conditions, it characterizes the imaging characteristics of the SAR under complex sea conditions, and improves the mathematical mapping relationship between the incident angle and the wave wavelength γ.

[0012] As a preferred embodiment of the present invention, the first acquisition module includes an interference source localization unit and a spectrum scanning unit, wherein: The interference source localization unit is used to construct three-dimensional coordinates to obtain the spatial location (x, y, z) of active interference sources at sea through radar reverse signal tracking and signal strength inversion algorithms. The active interference sources include signal base stations and communication radiation equipment established at sea. The sea surface is divided into a first region L1, a second region L2, a third region L3 and an nth region Ln. Each region contains i active interference sources, where i = 1…m_n. The spectrum scanning unit is used to scan the radar's current operating frequency band and its adjacent frequency bands in real time, identify the frequency shift phenomenon caused by the i-th active interference source in the n-th region Ln, and construct a frequency band offset dataset.

[0013] As a preferred embodiment of the present invention, the second acquisition module includes an environmental factor monitoring unit and a wave feature extraction unit, wherein: The environmental factor monitoring unit is used to collect real-time data on changes in the sea surface environment, including wave height h, wave wavelength γ and wind speed v, and to construct a sea surface disturbance dataset. The wave feature extraction unit is used to analyze the sea surface texture intensity T and wave slope based on SAR images, extract the spatial features of sea surface fluctuations, and construct a sea surface reflection dataset by combining wave height h, wave wavelength γ and wind speed v.

[0014] As a preferred embodiment of the present invention, the interference analysis and adaptive control module includes an active interference extraction unit and a passive interference extraction unit, wherein the active interference extraction unit includes a first comparison subunit, wherein: The active interference extraction unit is used to identify active interference sources distributed within the sea area, and to obtain the number of active interference sources through radar echo signal feature analysis. The specific calculation formula is as follows: Where N is the total number of regions. Let be the number of active interference sources in the nth region; By real-time scanning of the current SAR operating frequency band and its adjacent frequency bands, and using power spectral density analysis and energy mutation identification, the perturbation characteristics of the i-th active interference source in the n-th region Ln in the frequency band offset dataset are extracted to obtain the frequency band center value offset Δpyl. Combined with the total number of active interference sources Zs, after dimensionless processing, the frequency band offset coefficient Bdx is calculated. The specific calculation formula is as follows: in, Let be the energy weighting coefficient of the i-th active interference source in the n-th region Ln.

[0015] As a preferred embodiment of the present invention, the first comparison subunit is used to set a frequency band offset threshold B, and compare the frequency band offset coefficient Bdx with the frequency band offset threshold B, wherein the comparison rule is as follows: When Bdx > B × 120%, it is determined that the radar operating frequency band is subject to first-level interference, which causes the echo signal to be distorted and affects the image quality. The radar operating frequency band needs to be moved up or down, and the frequency range should be controlled within ±10MHz to ±30MHz. After the frequency band is adjusted, the spectrum should continue to be monitored. When B≤Bdx≤B×120%, it is determined that the radar operating frequency band is subject to secondary interference, resulting in unqualified images and difficulty in locating the interference source. The radar operating frequency band needs to be adjusted up or down by 5 to 10 MHz, and the image processing algorithm should be enhanced before monitoring can be continued to make timely adjustments. When Bdx < B, it is determined that the current radar operating frequency band is not affected by active interference sources, and the monitoring status is maintained.

[0016] As a preferred embodiment of the present invention, the passive interference extraction unit is used to identify the interference effects of the natural environment on SAR images. It obtains the wind speed v, wave height h, and wave wavelength γ at different wind speeds v from the sea surface disturbance dataset, and calculates the sea surface disturbance coefficient based on the texture intensity T in the SAR image after dimensionless processing. The specific calculation formula is as follows: Where ω=1; h is the wave height, γ is the wave wavelength, T is the texture intensity, R is the echo energy, and α1, α2, α3, β1, β2 are weighting coefficients. This represents the sea surface disturbance coefficient.

[0017] As a preferred embodiment of the present invention, the passive interference extraction unit further includes a second comparison subunit, which is used to set a sea surface disturbance threshold C and to set the sea surface disturbance coefficient. The second correction instruction is generated by comparing it with the sea surface disturbance threshold C. The specific comparison rules are as follows: When Hfx > C × 120%, it is determined that the sea surface disturbance causes level 3 interference to the SAR, resulting in speckle and imaging blur, and target boundary deformation. The incident angle of the SAR carried by the UAV needs to be adjusted to 35° to 45°, and the flight altitude of the UAV needs to be increased to 800m to 1000m. When C≤Hfx≤C×120%, it is determined that the sea surface disturbance causes level 4 interference to SAR, and the image shows stripes, bright spots and texture blurring. The incident angle needs to be adjusted to 40°~48°, and the SAR mid-range filtering and texture weighted noise reduction module should be activated. At the same time, the multi-frame image fusion mechanism should be started to extend the synthesis period to 3s. When Hfx < C, it is determined that the sea state does not interfere with the SAR image, the image is normal, and monitoring continues.

[0018] As a preferred embodiment of the present invention, the passive interference extraction unit further includes a sea surface reflection evaluation subunit; the sea surface reflection evaluation subunit is used to collect sea surface reflection brightness and radar echo energy, obtain wave height h and wave wavelength γ at different wind speeds v, as well as sea surface texture intensity T and wave slope from the sea surface reflection dataset, and calculate the sea surface reflection coefficient based on energy changes under natural disturbance conditions and after dimensionless processing, the specific calculation formula is as follows: in, This represents the actual radar echo energy received at present. For reference echo energy, This represents the sea surface brightness value in the current SAR image. The reference brightness value under undisturbed conditions, ω=1, This represents the sea surface reflectance.

[0019] As a preferred embodiment of the present invention, the sea surface reflection assessment subunit includes a third comparison subunit. The third comparison subunit is used to set a sea surface reflection threshold W and compare the sea surface reflection coefficient Fxs with the sea surface reflection threshold W to generate a third correction instruction. The specific comparison rule is as follows: When Fxs > W×120%, it is determined that the sea surface reflection causes level 5 interference to the SAR imaging quality. The radar polarization mode needs to be adjusted from single polarization to dual polarization, and the image dynamic range needs to be expanded from 8-bit to 12-bit. At the same time, the radar imaging window length needs to be compressed from 500m to 300m to ensure that the image quality is qualified. When W≤Fxs≤W×120%, it is determined that the sea surface reflection causes level 6 interference to SAR imaging. The radar transmit power needs to be reduced by 5% to 10%, the pulse repetition frequency (PRF) needs to be shortened from 1800Hz to 1600Hz, and the incident angle needs to be reduced from 45° to 35°. When Fxs < W, it is determined that the sea surface reflection does not interfere with the SAR image, the image is normal, and monitoring continues.

[0020] Compared with the relevant prior art, the beneficial effects of the present invention are: Active interference can be quantified and avoided in a closed loop: By using regional spectrum scanning and frequency band offset datasets, the disturbance characteristics of active interference sources in each region are extracted, the frequency band offset coefficient Bdx is constructed and compared, and the first correction command is triggered to realize the working frequency band up / down shift and parameter linkage adjustment, which significantly reduces spectrum congestion and image distortion caused by co-channel / out-of-band interference.

[0021] Passive sea state interference can be quantitatively assessed: Without changing the imaging process, the sea surface disturbance coefficient Hfx (which integrates wind speed v, wave height h, wavelength γ and image-side features such as T and R) is introduced to perform dimensionless quantification of sea state interference, providing a direct basis for whether to adjust the incident angle, flight altitude and filtering / fusion strategy, thereby reducing speckle enhancement and boundary blurring.

[0022] Early warning and correction of sea surface reflection impact: By using the sea surface reflection coefficient Fxs, abnormal changes in echo energy and image brightness are converted into discriminable signals, enabling early identification of problems such as bright bands and stripes caused by strong reflections. The system also links polarization mode, dynamic range and PRF parameters for correction, reducing post-processing costs.

[0023] Dual-criteria collaboration and parameter linkage bring stable imaging: Bdx and Hfx / Fxs are used for collaborative decision-making to form a closed loop of identification-decision-correction-re-evaluation; in addition to frequency band and incident angle, key parameters such as polarization, dynamic range, PRF, imaging window and flight altitude can be linked according to trigger level to ensure that the image quality remains consistent and comparable under different sea conditions and interference intensities.

[0024] The project is feasible and adaptable to UAV airborne applications: the system is modularly implemented, spectrum scanning and image feature extraction run in parallel, thresholds and weights are configurable, it is adapted to the reference altitude of UAVs and adjusted according to instructions, meeting the real-time / near real-time requirements of reclamation patrol and emergency monitoring, and has strong feasibility. Attached Figure Description

[0025] Figure 1 A flowchart of an adaptive anti-interference synthetic aperture radar remote sensing imaging system for land reclamation provided by the present invention; Figure 2 A schematic diagram illustrating the key physical characteristics of electromagnetic scattering at the sea surface in an embodiment of the present invention is provided. Figure 3 This invention provides a schematic diagram of the monitoring results of the data acquisition module in an embodiment. Figure 4 This invention provides a schematic diagram comparing interference analysis results with regional differences in embodiments. Figure 5 This invention provides a schematic diagram of the hierarchical correction instruction and parameter linkage strategy in an embodiment; Figure 6 This invention provides a schematic diagram of the multi-scale visualization results of maritime targets in an embodiment. Figure 7 A flowchart of SAR image processing is provided in an embodiment of the present invention. Detailed Implementation

[0026] The solutions provided by the present invention will be further described below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways and should not be construed as limited to the embodiments shown; rather, these embodiments provide those skilled in the art with implementation methods that meet applicable legal requirements.

[0027] Example 1: As Figure 1 As shown, the present invention provides a technical solution: an adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation, comprising a geometric optical model construction module, a first acquisition module, a second acquisition module, and an interference analysis module; T1: Geometric Optical Model Building Module, used to establish a radar wave propagation and reflection model in a marine environment, and to build a mathematical mapping relationship between the radar wave incident angle and the wave wavelength γ. The geometric optics model building module is also used to enable SAR to fly at a reference altitude on the sea surface via UAV, and can adjust the flight altitude according to the correction instructions generated by the interference analysis and adaptive control module; based on the wave height, incident wave angle and ground reflection conditions, it characterizes the imaging characteristics of SAR under complex sea conditions, and improves the mathematical mapping relationship between the incident angle and the wave wavelength γ.

[0028] T2: The first acquisition module is used to acquire the spatial location information of active interference sources based on the model described in T1, divide the sea surface into n regions, and use a spectrum scanning unit to scan the radar's current operating frequency band and its adjacent frequency bands in real time, identify the frequency shifts caused by active interference sources in each region, and construct a frequency band offset dataset; the first acquisition module includes an interference source localization unit and a spectrum scanning unit, wherein: The interference source localization unit is used to construct three-dimensional coordinates to obtain the spatial location (x, y, z) of active interference sources at sea through radar reverse signal tracking and signal strength inversion algorithms. The active interference sources include signal base stations and communication radiation equipment established at sea. The sea surface is divided into a first region L1, a second region L2, a third region L3, and an nth region Ln. Each region contains i active interference sources, where i = 1…m_n. The spectrum scanning unit is used to scan the radar's current operating frequency band and its adjacent frequency bands in real time, identify the frequency shift phenomenon caused by the i-th active interference source in the nth region Ln, and construct the frequency shift dataset.

[0029] T3: The second acquisition module is used to construct a sea surface disturbance dataset under different wind speeds (v), wave heights (h), and wave wavelengths (γ), and to construct a sea surface reflection dataset by combining sea surface texture intensity (T) and wave slope. The second acquisition module includes an environmental factor monitoring unit and a wave feature extraction unit, wherein: The environmental factor monitoring unit is used to collect real-time data on changes in the sea surface environment, including wave height h, wave wavelength γ, and wind speed v, and to construct a sea surface disturbance dataset. The wave feature extraction unit is used to analyze the sea surface texture intensity T and wave slope based on SAR images, extract the spatial features of sea surface fluctuations, and combine wave height h, wave wavelength γ, and wind speed v to construct a sea surface reflection dataset.

[0030] T4: Disturbance Analysis and Adaptive Control Module, which is used for: T4-1: Extract the perturbation features of the i-th active interference source in the n-th region Ln from the frequency band offset dataset in module T2, obtain the frequency band center value offset Δpyl, and combine it with the total number of active interference sources Zs, weighted by energy weight K_{n,i} and normalized by Zs to construct the frequency band offset coefficient Bdx, which serves as the core criterion for active anti-interference. T4-2: Perform dimensionless processing on the sea surface disturbance dataset and sea surface reflection dataset in module T3, calculate the sea surface disturbance coefficient Hfx and sea surface reflection coefficient Fxs respectively, and make collaborative decisions with the corresponding thresholds to generate passive anti-interference correction instructions. T4-3: Based on the determination results of Bdx, Hfx and Fxs, the radar operating frequency band and incident angle are corrected adaptively in a closed loop, and the associated imaging parameters are adjusted in a controlled manner to ensure the quality of remote sensing images.

[0031] The interference analysis and adaptive control module includes an active interference extraction unit and a passive interference extraction unit, wherein the active interference extraction unit includes a first comparison subunit, wherein: The active interference extraction unit is used to identify active interference sources distributed within the sea area, and to obtain the number of active interference sources through radar echo signal feature analysis. The specific calculation formula is as follows: Where N is the total number of regions. Let be the number of active interference sources in the nth region; By real-time scanning of the current SAR operating frequency band and its adjacent frequency bands, and using power spectral density analysis and energy mutation identification, the perturbation characteristics of the i-th active interference source in the n-th region Ln in the frequency band offset dataset are extracted to obtain the frequency band center value offset Δpyl; combined with the total number of active interference sources Zs, after dimensionless processing, the frequency band offset coefficient Bdx is calculated, and the specific calculation formula is as follows: in, Let be the energy weighting coefficient of the i-th active interference source in the n-th region Ln.

[0032] Example 2: The disturbance analysis and adaptive control module shown also includes the following modules: The first comparison subunit is used to set the frequency band offset threshold B, and compares the frequency band offset coefficient Bdx with the frequency band offset threshold B. The comparison rule is as follows: When Bdx > B × 120%, it is determined that the radar operating frequency band is subject to first-level interference, which causes the echo signal to be distorted and affects the image quality. The radar operating frequency band needs to be moved up or down, and the frequency range should be controlled within ±10MHz to ±30MHz. After the frequency band is adjusted, the spectrum should continue to be monitored. When B≤Bdx≤B×120%, it is determined that the radar operating frequency band is subject to secondary interference, resulting in unqualified images and difficulty in locating the interference source. The radar operating frequency band needs to be adjusted up or down by 5 to 10 MHz, and the image processing algorithm should be enhanced before monitoring can be continued to make timely adjustments. When Bdx < B, it is determined that the current radar operating frequency band is not affected by active interference sources, and the monitoring status is maintained.

[0033] Table 1: Frequency Band Offset Coefficient Example table

[0034] Table 1 shows example results of the frequency band offset coefficient Bdx calculated under the conditions of the total number Zs of active interference sources, the corresponding frequency band center value offset, and the energy weighting coefficient under different sea surface areas Ln, where the frequency band offset threshold B is 2.4. The results in the table show that when Bdx = 2.46 in a certain area, which is between B and B × 120%, it is judged as Level 2 interference; when Bdx = 2.01, which is less than the threshold B, it is judged as no interference; and when Bdx = 3.17 or 3.24, which is greater than B × 120%, it is judged as Level 1 interference. This verifies the effectiveness of the frequency band offset coefficient Bdx and the corresponding comparison rule in determining the level of active interference.

[0035] The passive interference extraction unit is used to identify the interference effects of the natural environment on SAR images. It obtains wind speed v, wave height h, and wave wavelength γ at different wind speeds v from the sea surface disturbance dataset, and calculates the sea surface disturbance coefficient based on the texture intensity T in the SAR image after dimensionless processing. The specific calculation formula is as follows: Where ω=1; h is the wave height, γ is the wave wavelength, T is the texture intensity, R is the echo energy, and α1, α2, α3, β1, β2 are weighting coefficients. This represents the sea surface disturbance coefficient.

[0036] Table 2: Example Table of Sea Surface Disturbance Coefficient Hfx

[0037] As shown in Table 2, under the condition that the natural environmental parameters such as wave height h, wave wavelength γ, texture intensity T, echo energy R, and wind speed v are determined, the corresponding sea surface disturbance coefficient Hfx is calculated, and C=4.0 is used as the sea surface disturbance threshold for judgment. When Hfx is less than the threshold C (such as serial numbers 1, 3, 6, and 8), it is determined that the sea state has basically no interference with the SAR image; when Hfx is in the range of C to C×120% (such as serial number 2), it corresponds to level four interference; when Hfx is greater than C×120% (such as serial numbers 4, 5, 7, 9, and 10), it is determined to be level three interference. It can be seen that the calculation model of Hfx and the threshold comparison rule can accurately reflect the impact of sea surface disturbance on SAR imaging quality under different sea states.

[0038] The passive interference extraction unit further includes a second comparison subunit, which is used to set the sea surface disturbance threshold C and to set the sea surface disturbance coefficient. The second correction instruction is generated by comparing it with the sea surface disturbance threshold C. The specific comparison rules are as follows: When Hfx > C × 120%, it is determined that the sea surface disturbance causes level 3 interference to the SAR, resulting in speckle and imaging blur, and target boundary deformation. The incident angle of the SAR carried by the UAV needs to be adjusted to 35° to 45°, and the flight altitude of the UAV needs to be increased to 800m to 1000m. When C≤Hfx≤C×120%, it is determined that the sea surface disturbance causes level 4 interference to SAR, and the image shows stripes, bright spots and texture blurring. The incident angle needs to be adjusted to 40°~48°, and the SAR mid-range filtering and texture weighted noise reduction module should be activated. At the same time, the multi-frame image fusion mechanism should be started to extend the synthesis period to 3s. When Hfx < C, it is determined that the sea state does not interfere with the SAR image, the image is normal, and monitoring continues.

[0039] The passive interference extraction unit also includes a sea surface reflection assessment subunit. This subunit is used to collect sea surface reflection brightness and radar echo energy, obtain wave height h and wave wavelength γ at different wind speeds v from the sea surface reflection dataset, as well as sea surface texture intensity T and wave slope. Based on energy changes under natural disturbance conditions and after dimensionless processing, the sea surface reflection coefficient is calculated. The specific calculation formula is as follows: in, This represents the actual radar echo energy received at present. For reference echo energy, This represents the sea surface brightness value in the current SAR image. The reference brightness value under undisturbed conditions, ω=1, This represents the sea surface reflectance.

[0040] The sea surface reflection assessment subunit includes a third comparison subunit. This third comparison subunit is used to set the sea surface reflection threshold W and compare the sea surface reflection coefficient Fxs with the sea surface reflection threshold W to generate a third correction instruction. The specific comparison rules are as follows: When Fxs > W×120%, it is determined that the sea surface reflection causes level 5 interference to the SAR imaging quality. The radar polarization mode needs to be adjusted from single polarization to dual polarization, and the image dynamic range needs to be expanded from 8-bit to 12-bit. At the same time, the radar imaging window length needs to be compressed from 500m to 300m to ensure that the image quality is qualified. When W≤Fxs≤W×120%, it is determined that the sea surface reflection causes level 6 interference to SAR imaging. The radar transmit power needs to be reduced by 5% to 10%, the pulse repetition frequency (PRF) needs to be shortened from 1800Hz to 1600Hz, and the incident angle needs to be reduced from 45° to 35°. When Fxs < W, it is determined that the sea surface reflection does not interfere with the SAR image, the image is normal, and monitoring continues.

[0041] Table 3: Example Table of Sea Surface Disturbance Coefficient Hfx

[0042] As shown in Table 3, the corresponding sea surface reflection coefficient Fxs was calculated under different wind speeds v, wave heights h, wave wavelengths γ, and combinations of sea surface brightness and radar echo energy. A sea surface reflection threshold of W=0.3 was used for judgment. When Fxs is less than the threshold W (Group 1), sea surface reflection is considered to have virtually no interference with the SAR image; when Fxs is in the range of W to W×120% (Group 2), it corresponds to Level 6 interference; when Fxs is greater than W×120% (Groups 3-10), it is judged as Level 5 interference. Therefore, the calculation model of Fxs and the threshold comparison rule can effectively distinguish the interference levels under different sea surface reflection conditions, providing a quantitative basis for subsequent polarization mode adjustment and imaging parameter optimization.

[0043] In this embodiment, by setting a comparison between the sea surface reflectance coefficient and the sea surface reflectance threshold, the impact of sea surface reflection on image quality can be accurately assessed. Then, by dividing the image into different reflectance coefficient ranges, graded interference processing (level 5 interference, level 6 interference, and no interference) is implemented. This mechanism can execute flexible and efficient correction commands according to different interference levels, effectively avoiding misjudgments or improper processing. By dynamically adjusting based on real-time measured sea surface reflectance coefficients, the system can quickly respond and accurately judge under different sea conditions, and execute appropriate image restoration and frequency band adjustment measures for different interference levels. This function ensures the system's image processing capabilities in complex environments, not only improving data quality but also guaranteeing adaptability to complex sea surface conditions.

[0044] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0045] Example 3: This example demonstrates the verification of airborne UAV SAR measurements and coordinated control in a key nearshore reclamation area. The platform is configured according to the invention's specifications, including a geometric optics model construction module, a first acquisition module, a second acquisition module, and an interference analysis and adaptive control module. The UAV performs strip-shaped cruise at a reference altitude. The SAR operates at 5–10 GHz, switching between single and dual polarization, with the incident angle adjustable within the range of 35°–48°. The pulse repetition frequency (PRF) supports adaptive variation between 1600–1800 Hz, and the image dynamic range supports both 8-bit and 12-bit levels. All system symbols, parameters, and threshold names are consistent with the invention's specifications to ensure correspondence between implementation and claims.

[0046] Before the operation, the geometric optics model building module performed a preliminary convergence test on the sea surface scattering mechanism based on historical sea conditions and platform calibration. Figure 2 The prior relationships show that: as wind speed increases, the optimal working range of the incident angle converges exponentially (indicating an approach from approximately 45° to a smaller angle; in engineering, a margin of ≥35° is reserved for both radiation and geometry); the wave wavelength increases approximately linearly with wind speed (indicating an extension from the kilometer level to the tens of kilometers level), suggesting that multi-scale fluctuations in strong winds and sea conditions will significantly alter the stability of the echo phase / amplitude; the variation of the horizontal and vertical polarization reflection coefficients with the incident angle intersects near Brewster's angle, suggesting that polarization switching has a directional effect on suppressing specular components and enhancing rough scattering; Bragg scattering attenuates significantly with increasing incident angle (indicating an attenuation from approximately -25dB to -78dB), providing a physical boundary for subsequent upper limits of the incident angle to participate in radiation correction. This prior set is used to constrain the adaptive search domain of the incident angle and polarization, and provides an engineering baseline for the initial weighting of Hfx and Fxs.

[0047] During field data collection, the system simultaneously runs the first and second data collection modules. Figure 3The spectrum scan detected three significant active interference peaks within the 5–10 GHz range, approximately at 5 GHz, 6 GHz, and 9.5 GHz, all exceeding the -60 dB detection threshold. The corresponding frequency band offsets were reproducible across the four sub-regions L1–L4, supporting subsequent regionalized calculation and dynamic comparison of Bdx. On the sea surface, real-time wind speeds were concentrated between 10 and 13 m / s, with wave heights primarily distributed between 1.81 and 1.82 m. These, combined with image-side texture intensity T and echo energy R, formed the necessary inputs for Hfx and Fxs. To ensure the consistency of the parameter closed-loop, all active blocks were divided into L1–L4 grids, and active / passive elements and image features were stored in the database within the same time window to avoid criterion drift caused by cross-time window comparisons.

[0048] After data collection, the interference analysis and adaptive control module statistically analyzes the key indicators of the four regions. Figure 4 Bdx fluctuates stably in the L1–L4 range, with an average of approximately 2.4 MHz. L2 and L3 show slightly higher values, indicating more active interference. Hfx has an average of approximately 4.0 in all four regions, with a peak value of approximately 5.5 in L3, indicating the strongest sea state disturbance in this region. Fxs has an average of approximately 0.3 in all four regions, with a similarly high value in L3, suggesting enhanced reflection on both the brightness and energy sides. The count of active interference sources remains at 5–6 orders of magnitude in each region, corroborating the spatial distribution of Bdx. Normalized comparison of the three indicators shows that L3 consistently increases in Bdx, Hfx, and Fxs, making it a priority area for intervention. Correlation heatmaps reveal a correlation coefficient of approximately 0.75 between Bdx and Hfx, and approximately 0.84 between Hfx and Fxs, indicating that enhanced sea state is often coupled with enhanced reflection, and is exacerbated by active interference. A single criterion is insufficient to avoid misjudgment; therefore, a collaborative decision-making process using three criteria is necessary to drive corrective commands.

[0049] Based on the hierarchical threshold framework ( Figure 5This embodiment demonstrates a complete closed-loop control at L3. During initial evaluation, Bdx at L3 falls into the red zone (above B×120%), Hfx is also in the red zone (above C×120%), and Fxs is located at the upper yellow edge (close to W×120%). The system first performs active-side safety avoidance: shifting the operating frequency band from around 6GHz, with the displacement controlled within the red zone strategy range of ±10 to ±30MHz. After rescanning, the active peak significantly decreases, and Bdx drops to the yellow zone (approximately 1.7MHz). Subsequently, sea state disturbances are addressed: the incident angle is adjusted from the baseline pose to approximately 38°, and the flight altitude is increased to approximately 900m. Simultaneously, median filtering and texture-weighted noise reduction are activated, and multi-frame fusion with a 3-second synthesis cycle is initiated. Hfx falls from the red zone back to the yellow zone (approximately 4.2). For the reflection side, the system fine-tunes the energy budget without sacrificing radiation safety: the transmit power is reduced by approximately 8%, the PRF is lowered from 1800Hz to 1600Hz, and the incident angle is fine-tuned to approximately 37°, causing Fxs to fall back to the green range (approximately 0.28). After one closed-loop operation, the stripes and bright bands are significantly alleviated, and the geometric boundaries are more consistent, eliminating the need to trigger higher-level polarization switching, 12-bit expansion, and window compression strategies; the system enters steady-state cruise, only performing fine-tuning and re-evaluation when Bdx, Hfx, and Fxs approach the threshold.

[0050] The above adjustments directly improved the image performance on the business side. Figure 6 , Figure 7 In the SSDD offshore building scenario, after logarithmic scaling and enhanced display of the original image, the structure and boundaries of strongly scattering targets are clearer, and false-color enhancement significantly highlights offshore facilities in the red channel. Compared with before loop closure, speckle aggregation and bright bands are effectively suppressed, which is beneficial for subsequent vectorized boundary extraction and target detail interpretation. In the end-to-end process, the analysis of the original data first verifies the consistency between the frequency domain peak and energy concentration band and the spectral scanning results. Under the steady state of PRF=1600Hz, the background spectrum is more convergent after frequency domain filtering and noise reduction. With azimuth compression and polar coordinate transformation combined with an incident angle of 37°~40° working window, the geometric and radiometric performance tends to be stable. Finally, the combination of Lee filtering and CFAR detection is used to accurately label the strongly scattering units of offshore facilities and reclamation structures without amplifying false alarms, supporting continuous change detection and consistency assessment.

[0051] To ensure reproducibility, the key boundary conditions for this embodiment are as follows: test frequency band 5–10 GHz, initial active peak located at 5 / 6 / 9.5 GHz and exceeding the -60 dB threshold; wind speed 10–13 m / s, wave height 1.81–1.82 m, and wave wavelength γ according to... Figure 2Prior estimation; regional division L1–L4, with 5–6 active interference sources detected in each region; typical indicators during the statistical period are a Bdx average of approximately 2.4 MHz, an Hfx average of approximately 4.0, and an Fxs average of approximately 0.3, with L3 representing a high-interference region. Triggering actions follow a tiered rule: frequency band fine-tuning of 5–10 MHz or shifting ±10–±30 MHz; closed-loop search within an incident angle of 35°–48°, with altitude increased to 800–1000 m as needed; if necessary, a 5%–10% reduction in transmit power and a PRF reduction from 1800 Hz to 1600 Hz are implemented, and a combined strategy of dual polarization, 12-bit, and a 300 m window is activated during Level 5 interference. The above process is consistent with… Figures 2-7 One-to-one correspondence forms a complete link from physical priors, data acquisition, three-criteria evaluation to linkage correction and operational imaging, proving that the present invention can stably realize the engineering implementation of land reclamation monitoring under complex sea conditions and mixed interference.

[0052] The above embodiments merely illustrate implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. An adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation, characterized in that: include: T1: Geometric Optical Model Building Module, used to establish a radar wave propagation and reflection model in a marine environment, and to build a mathematical mapping relationship between the radar wave incident angle and the wave wavelength γ. T2: The first acquisition module is used to acquire the spatial location information of active interference sources based on the model in module T1, divide the sea surface into n regions, and use the spectrum scanning unit to scan the radar's current operating frequency band and its adjacent frequency bands in real time, identify the frequency shift caused by active interference sources in each region, and construct a frequency band offset dataset. T3: The second acquisition module is used to construct a sea surface disturbance dataset under different wind speeds v, wave heights h, and wave wavelengths γ, and to construct a sea surface reflection dataset by combining the sea surface texture intensity T and the wave surface slope. T4: Disturbance Analysis and Adaptive Control Module, which is used for: T4-1: Extract the perturbation features of the i-th active interference source in the n-th region Ln from the frequency band offset dataset in module T2, obtain the frequency band center value offset Δpyl, and combine it with the total number of active interference sources Zs, weighted by energy weight K_{n,i} and normalized by Zs to construct the frequency band offset coefficient Bdx, which serves as the core criterion for active anti-interference. T4-2: Perform dimensionless processing on the sea surface disturbance dataset and sea surface reflection dataset in module T3, calculate the sea surface disturbance coefficient Hfx and sea surface reflection coefficient Fxs respectively, and make collaborative decisions with the corresponding thresholds to generate passive anti-interference correction instructions. T4-3: Based on the determination results of Bdx, Hfx and Fxs, the radar operating frequency band and incident angle are corrected adaptively in a closed loop, and the associated imaging parameters are adjusted in a controlled manner to ensure the quality of remote sensing images.

2. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of the sea according to claim 1, characterized in that: The geometric optics model construction module is also used to enable SAR to fly at a reference altitude on the sea surface via UAV, and can adjust the flight altitude according to the correction instructions generated by the interference analysis and adaptive control module; based on the wave height, incident wave angle and ground reflection conditions, it characterizes the imaging characteristics of SAR under complex sea conditions, and improves the mathematical mapping relationship between the incident angle and the wave wavelength γ.

3. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of the sea according to claim 2, characterized in that: The first acquisition module includes an interference source localization unit and a spectrum scanning unit, wherein: The interference source localization unit is used to construct three-dimensional coordinates to obtain the spatial location (x, y, z) of active interference sources at sea through radar reverse signal tracking and signal strength inversion algorithms; wherein, active interference sources include signal base stations and communication radiation equipment established at sea; the sea surface is divided into the first region L1, the second region L2, the third region L3 and the nth region Ln, each region contains i active interference sources, where i = 1…m_n; The spectrum scanning unit is used to scan the radar's current operating frequency band and its adjacent frequency bands in real time, identify the frequency shift phenomenon caused by the i-th active interference source in the n-th region Ln, and construct a frequency band offset dataset.

4. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of the sea according to claim 3, characterized in that: The second acquisition module includes an environmental factor monitoring unit and a wave feature extraction unit, wherein: The environmental factor monitoring unit is used to collect real-time data on changes in the sea surface environment, including wave height h, wave wavelength γ and wind speed v, and to construct a sea surface disturbance dataset. The wave feature extraction unit is used to analyze the sea surface texture intensity T and wave slope based on SAR images, extract the spatial features of sea surface fluctuations, and construct a sea surface reflection dataset by combining wave height h, wave wavelength γ and wind speed v.

5. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of the sea according to claim 4, characterized in that: The interference analysis and adaptive control module includes an active interference extraction unit and a passive interference extraction unit, wherein the active interference extraction unit includes a first comparison subunit, wherein: The active interference extraction unit is used to identify active interference sources distributed within the sea area, and to obtain the number of active interference sources through radar echo signal feature analysis. The specific calculation formula is as follows: Where Zs represents the total number of active interference sources, and N represents the total number of regions. Let be the number of active interference sources in the nth region; By real-time scanning of the current SAR operating frequency band and its adjacent frequency bands, and using power spectral density analysis and energy mutation identification, the perturbation characteristics of the i-th active interference source in the n-th region Ln in the frequency band offset dataset are extracted to obtain the frequency band center value offset Δpyl. Combined with the total number of active interference sources Zs, after dimensionless processing, the frequency band offset coefficient Bdx is calculated. The specific calculation formula is as follows: in, Δpyl is the energy weighting coefficient of the i-th active interference source in the n-th region Ln, and Δpyl is the frequency band center value offset.

6. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of the sea according to claim 5, characterized in that: The first comparison subunit is used to set the frequency band offset threshold B, and compare the frequency band offset coefficient Bdx with the frequency band offset threshold B. The comparison rule is as follows: When Bdx > B × 120%, it is determined that the radar operating frequency band is subject to first-level interference, which causes the echo signal to be distorted and affects the image quality. The radar operating frequency band needs to be moved up or down, and the frequency range should be controlled within ±10MHz to ±30MHz. After the frequency band is adjusted, the spectrum should continue to be monitored. When B≤Bdx≤B×120%, it is determined that the radar operating frequency band is subject to secondary interference, resulting in unqualified images and difficulty in locating the interference source. The radar operating frequency band needs to be adjusted up or down by 5 to 10 MHz, and the image processing algorithm should be enhanced before monitoring can be continued to make timely adjustments. When Bdx < B, it is determined that the current radar operating frequency band is not affected by active interference sources, and the monitoring status is maintained.

7. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of the sea according to claim 5, characterized in that: The passive interference extraction unit is used to identify the interference effects of the natural environment on SAR images. It obtains the wind speed v, wave height h, and wave wavelength γ at different wind speeds v from the sea surface disturbance dataset, and calculates the sea surface disturbance coefficient based on the texture intensity T in the SAR image after dimensionless processing. The specific calculation formula is as follows: Where ω=1, h is the wave height, γ is the wave wavelength, T is the texture intensity, R is the echo energy, and α1, α2, α3, β1, and β2 are all weighting coefficients. This represents the sea surface disturbance coefficient.

8. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of land as described in claim 5, characterized in that: The passive interference extraction unit further includes a second comparison subunit, which is used to set a sea surface disturbance threshold C and to set the sea surface disturbance coefficient. The second correction instruction is generated by comparing it with the sea surface disturbance threshold C. The specific comparison rules are as follows: When Hfx > C × 120%, it is determined that the sea surface disturbance causes level 3 interference to the SAR, resulting in speckle and imaging blur, and target boundary deformation. The incident angle of the SAR carried by the UAV needs to be adjusted to 35° to 45°, and the flight altitude of the UAV needs to be increased to 800m to 1000m. When C≤Hfx≤C×120%, it is determined that the sea surface disturbance causes level 4 interference to SAR, and the image shows stripes, bright spots and texture blurring. The incident angle needs to be adjusted to 40°~48°, and the SAR mid-range filtering and texture weighted noise reduction module should be activated. At the same time, the multi-frame image fusion mechanism should be started to extend the synthesis period to 3s. When Hfx < C, it is determined that the sea state does not interfere with the SAR image, the image is normal, and monitoring continues.

9. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of land as described in claim 5, characterized in that: The passive interference extraction unit also includes a sea surface reflection evaluation subunit; the sea surface reflection evaluation subunit is used to collect sea surface reflection brightness and radar echo energy, obtain wave height h and wave wavelength γ at different wind speeds v from the sea surface reflection dataset, as well as sea surface texture intensity T and wave surface slope, and calculate the sea surface reflection coefficient based on energy changes under natural disturbance conditions and after dimensionless processing. The specific calculation formula is as follows: in, This represents the actual radar echo energy received at present. For reference echo energy, This represents the sea surface brightness value in the current SAR image. The reference brightness value under undisturbed conditions, ω=1, This represents the sea surface reflectance.

10. The adaptive anti-interference synthetic aperture radar remote sensing imaging system for reclamation of land as described in claim 9, characterized in that: The sea surface reflection assessment subunit includes a third comparison subunit, which is used to set a sea surface reflection threshold W and compare the sea surface reflection coefficient Fxs with the sea surface reflection threshold W to generate a third correction instruction. The specific comparison rules are as follows: When Fxs > W×120%, it is determined that the sea surface reflection causes level 5 interference to the SAR imaging quality. The radar polarization mode needs to be adjusted from single polarization to dual polarization, and the image dynamic range needs to be expanded from 8-bit to 12-bit. At the same time, the radar imaging window length needs to be compressed from 500m to 300m to ensure that the image quality is qualified. When W≤Fxs≤W×120%, it is determined that the sea surface reflection causes level 6 interference to SAR imaging. The radar transmit power needs to be reduced by 5% to 10%, the pulse repetition frequency (PRF) needs to be shortened from 1800Hz to 1600Hz, and the incident angle needs to be reduced from 45° to 35°. When Fxs < W, it is determined that the sea surface reflection does not interfere with the SAR image, the image is normal, and monitoring continues.

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