A multi-band track SAR equipment roadbed moisture content inversion method, medium and equipment

Abstract

The present invention relates to the technical field of roadbed moisture content measurement, and specifically to a method, medium, and equipment for inverting roadbed moisture content using a multi-band orbital SAR device. The method comprises the following steps: the orbital SAR device collects road surface image data of different bands; accurately matches the positions of corresponding pixels in the primary image and the secondary image of the road surface image data of the same band to achieve image registration; performs multi-view processing on the registered data; performs interference processing on the road surface image data to generate an interference pattern; performs coherence estimation on the differential interference pattern; and calculates the phase closure difference of the pixel triplets using complex coherence. The present invention fully utilizes the multi-band information of the multi-band synthetic aperture radar and achieves the inversion of the roadbed moisture content based on the phase change and phase closure difference of the interference patterns of multiple images.

Description

Technical Field

[0001] The present invention relates to the technical field of roadbed moisture content measurement, and in particular to a roadbed moisture content inversion method, medium and equipment using a multi-band track SAR device. Background Art

[0002] With the rapid development of transportation infrastructure, the health of roadbeds directly impacts road lifespan and driving safety. Roadbed moisture content is a key parameter for assessing structural stability. Excessively high moisture content can lead to softening, subsidence, and even collapse. This is particularly true during rainy seasons or in areas with frequent groundwater level fluctuations. Real-time moisture content monitoring is crucial for disaster warning and maintenance decision-making.

[0003] Traditional methods for measuring roadbed moisture content (such as borehole sampling, ground-penetrating radar, or infrared remote sensing) have significant drawbacks: borehole sampling damages the roadbed structure and is inefficient; ground-penetrating radar is limited by its depth and signal attenuation in complex media; and infrared remote sensing is susceptible to weather interference and suffers from insufficient spatial resolution. In recent years, synthetic aperture radar (SAR) technology has been increasingly used for surface and shallow soil moisture monitoring due to its all-weather, high-resolution, and penetrating capabilities. SAR is an advanced radar imaging technology that scans a target area with a radar beam, enabling high-resolution ground imagery in all weather conditions. Multi-band SAR utilizes the characteristics of electromagnetic waves in different bands to detect target objects and the environment. This provides richer surface information and target characteristics, improving detection and analysis accuracy, and enabling more comprehensive and accurate remote sensing information. It holds broad application prospects in areas such as soil moisture, glaciers, and geophysical monitoring. However, existing SAR data processing platforms are mostly designed for general remote sensing, lacking specialized algorithms for roadbed moisture content, and inadequate real-time processing capabilities. Furthermore, multi-band SAR data fusion and calibration technologies are immature, and collaborative inversion mechanisms across different frequency bands (such as L-band, C-band, and X-band) have yet to be effectively established, limiting the depth and reliability of moisture content inversion.

[0004] Therefore, a multi-band track SAR roadbed moisture inversion method and supporting system are urgently needed to break through the bottleneck of adaptability of existing technologies to complex roadbed environments, and at the same time develop lightweight hardware equipment and highly compatible storage media to achieve low-cost, high-precision, real-time monitoring of large-scale roadbed networks. Summary of the Invention

[0005] The present invention aims to provide a method, medium and equipment for inverting the water content of roadbed using a multi-band track SAR device. The specific technical solution is as follows:

[0006] A method for inverting roadbed moisture content using a multi-band track SAR device includes the following steps:

[0007] Collect road image data in different bands through orbital SAR equipment;

[0008] Accurately match the positions of corresponding pixels in the primary and secondary images of the same band road image data to achieve image registration;

[0009] Perform multi-view processing on the registered road image data;

[0010] Perform interference processing on road surface image data to generate interference maps;

[0011] Estimating the coherence of the interferogram;

[0012] The phase closure difference of the pixel triplet is calculated using complex coherence, and the corresponding closure moisture content is obtained by fitting the phase closure difference.

[0013] Preferably, the road surface image data of different bands collected by the track SAR equipment is specifically:

[0014] The road surface echo signals are acquired using the HH, HV, VH, and VV polarization channels of the S-band and L-band SAR sensors to obtain road surface image data.

[0015] Preferably, the positions of corresponding pixels in the primary image and the secondary image of the road image data of the same band are accurately matched and image registration is achieved specifically as follows:

[0016] Select some rows of the image as the reference area, traverse the possible translation amounts, calculate the mean square error of the two images after translation, and find the translation value that minimizes the mean square error; and according to the translation value, crop and fill zero operations on one of the images to align it with the other image in azimuth and achieve registration of the two images.

[0017] Preferably, the multi-view processing of the registered road image data is specifically performed as follows:

[0018] In azimuth and slant range directions, Average processing and The image size after multi-view processing is the same as the original image size. .

[0019] Preferably, interferometric processing of road surface image data is specifically performed as follows:

[0020] The image is divided into multiple small blocks according to the azimuth and range directions. The coherence of the two images is calculated for each small block. The interference value is obtained by complex conjugate multiplication and normalization, and an interference pattern is generated.

[0021] Preferably, performing coherence estimation on the interference pattern is specifically:

[0022] The coherence formula is used to calculate a coherence image, which is used to represent the coherence degree of different regions.

[0023] Preferably, the phase closure error of the pixel triplet is calculated using complex coherence:

[0024] For any triplet of three SAR images, the bicoherence is defined as the product of two complex coherences multiplied by the complex conjugate of the third, and the closed phase is the argument of the bicoherence.

[0025] The present invention also provides a readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the above-mentioned method for inverting the water content of a roadbed using a multi-band track SAR device.

[0026] The present invention also provides an electronic device comprising: at least one processor, at least one memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, a multi-band track SAR device roadbed moisture content inversion method is performed.

[0027] The application of the technical solution of the present invention has the following beneficial effects:

[0028] A method for inverting roadbed moisture content using a multi-band orbital SAR device includes: collecting road surface image data of different bands using an orbital SAR device; accurately matching the positions of corresponding pixels in the primary and secondary images of the same-band road surface image data to achieve image registration; performing multi-view processing on the registered road surface image data; interferometrically processing the road surface image data to generate an interferogram; estimating the coherence of the interferogram; and calculating the phase closure error of pixel triplets using complex coherence. The method of the present invention fully utilizes the sensitivity of orbital SAR technology to surface characteristics and its limited surface penetration capability, and utilizes the multi-band information of a multi-band synthetic aperture radar to invert roadbed moisture content based on the phase changes and phase closure errors of the multiple image interferograms. Multiple field tests for inverting roadbed moisture content have been conducted, verifying the feasibility of using multi-band synthetic aperture radar full-polarimetric measurement technology for inverting roadbed moisture content. The method offers high resolution, high flexibility, and fast response speed, providing an efficient and reliable solution for roadbed moisture content detection.

[0029] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 1 is a flow chart of a multi-band track SAR roadbed moisture inversion method according to an embodiment of the present invention;

[0032] Figure 2 is an intensity distribution diagram of 15 sets of experimental data acquired by the L-band SAR sensor carried on the orbital platform in an embodiment of the present invention;

[0033] Figure 3 is a full polarimetric diagram of the 15th set of experimental data acquired by the L-band SAR sensor carried on the orbital platform in an embodiment of the present invention;

[0034] Figure 4 is the corresponding relationship between the roadbed moisture content and the average strength of 15 groups of experimental areas after image registration processing in an embodiment of the present invention;

[0035] Figure 5 is an interference phase image generated based on the primary-secondary image interference pair in an embodiment of the present invention;

[0036] Figure 6 is a distribution diagram of the coherence coefficient of the orbital SAR interferometer according to an embodiment of the present invention;

[0037] Figure 7 is a phase closure diagram based on multi-view ratio 3-3 processing in an embodiment of the present invention;

[0038] Figure 8 3 is a fitting relationship diagram between the phase closure difference and the closure moisture content based on multi-view ratio processing in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] In one embodiment, see Figure 1 The multi-band track SAR roadbed moisture content inversion method of the present application is specifically a roadbed moisture content inversion method based on L-band track SAR, comprising the following steps:

[0041] S1: Track SAR collects road image data.

[0042] The ultra-high spatial resolution surface echo signals are obtained using the four polarization channels of HH, HV, VH, and VV of the L-band SAR sensor mounted on the orbital equipment.

[0043] Input the raw data parameter file, including the azimuth offset, step size, range starting point, and radar system parameters (such as data length, pulse width, bandwidth, carrier frequency, sampling rate, pulse repetition frequency, etc.). Based on the input parameters, the radar wavelength, modulation frequency, range frequency axis, and corresponding range axis are calculated.

[0044] Data processing is performed in both azimuth and range directions. In azimuth, the pulse index range, time axis, and corresponding antenna position are calculated; in range, effective range data is intercepted based on the experimental scenario.

[0045] According to the input polarization mode, determine the polarization channel to be processed (such as 、 、 、 ).

[0046] Based on the pulse index and polarization channel, the corresponding radar echo data is read and subjected to a Fast Fourier Transform (FFT) to obtain frequency domain data. The residual video phase (RVP) term in the range dimension is removed to correct the phase error. The range data after RVP removal is upsampled to improve imaging resolution.

[0047] For each azimuth pulse and each imaging point, the distance from the transmitting antenna to the target point, the distance from the receiving antenna to the target point, and the two-way distance are calculated. Based on the upsampled distance axis, the corresponding distance index is found. Based on this, the corresponding radar echo data is extracted from the upsampled data and multiplied by the phase compensation term. The cycle is repeated, and after completing the back-projection calculation for all azimuth pulses and polarization channels, the final imaging result is obtained. In this embodiment, a total of 15 sets of data are processed, and the intensity map obtained is as follows: Figure 2 Taking the 15th set of data images as an example, the intensity images after processing of the four polarization channels HH, HV, VH, and VV are shown in Figure 2. Figure 3 shown.

[0048] S2: Perform image registration on the image data.

[0049] It should be noted that the SAR sensor moves back and forth at a constant speed on the track, so image registration only needs to be performed in the azimuth direction.

[0050] Furthermore, the registration is performed in the azimuth direction. Some rows of the image are selected as the reference area, and the possible translation amounts are traversed. The mean square error (MSE) of the two images after translation is calculated to find the translation value that minimizes the error. One of the images is cropped and padded with zeros according to the translation value to align it with the other image in the azimuth direction, thus achieving registration of the two images.

[0051] In this implementation case, 15 sets of data were processed. After image registration, the relationship between the average strength of the experimental area and the water content of the roadbed was as follows: Figure 4 As shown in the figure, the average strength generally increases with increasing moisture content. The fitting equation can be used to predict the average strength at different moisture contents. Combining the residual sum of squares and the coefficient of determination further evaluates the model's fit to the data, providing a reference for studying the relationship between average strength and moisture content.

[0052] S3: Perform multi-view processing on the registered data.

[0053] It should be noted that the process of the multi-look processing algorithm is similar to the averaging of N independent sampling signals. It can be processed by multi-look averaging of independent SAR intensity images, or by multi-look averaging of the real and imaginary parts of the SAR complex image.

[0054] Furthermore, the multi-look processing algorithm in this embodiment specifically performs N-view averaging and M-view averaging in the azimuth and slant range directions, respectively. The size of the image after multi-look processing is 1 / NM of the original image. Multi-look processing is currently the most effective method for suppressing speckle noise. After multi-look processing, the variance of speckle noise can be reduced.

[0055] Specifically, azimuth multi-view synthesis: In the azimuth direction, weighted averaging of adjacent pixels is performed, which can be expressed by the following formula:

[0056] ;

[0057] in, is the original image at position , The amplitude value at ; is the azimuth viewing number, indicating how many pixels are averaged in the azimuth direction; Indicates that the image after multi-view processing is at position , The pixel value at The weighted average of adjacent pixels is obtained; The index of the pixel in the azimuth direction, ranging from 0 to .

[0058] Slant range multi-view synthesis: In the slant range direction, the pixels in the slant range direction are weighted averaged. The formula is as follows:

[0059] ;

[0060] in, It is the distance-wise number, indicating how many pixels are averaged in the distance direction. is the index of the pixel in the distance up, ranging from 0 to .

[0061] S4: Perform interference processing on the road surface image data to generate an interference phase map.

[0062] The main image is complex conjugate multiplied by each secondary image to obtain an interference diagram representing the phase difference between the two images. In this embodiment, a total of 15 sets of data are processed, with the first set of image data as the main image and the remaining 14 sets as secondary images. After image registration and multi-view data processing, the main image and the secondary image are interfered. The obtained interference phase diagram is as follows: Figure 5 As shown, the sub-images from left to right and from top to bottom are the interference phase images of the 1st image and the 2nd image, the 3rd image, ..., the 15th image.

[0063] S5: Estimating the coherence of the interference pattern.

[0064] The image is divided into multiple small blocks according to the azimuth and range directions. The coherence of the two images is calculated for each small block. That is, the coherence coefficient is obtained by complex conjugate multiplication and normalization. If the number of valid pixels in the small block is less than half, the coherence coefficient is set to 0. Finally, a coherence map representing the coherence of the two images is generated. The specific formula is as follows:

[0065] ;

[0066] in, It represents the coherence coefficient, which is used to measure the degree of coherence between two SAR images. Its value range is generally between 0 and 1. The closer the value is to 1, the better the coherence is, which reflects that the similarity of the corresponding objects in the two images is higher. and Represent the complex forms of two SAR images respectively. Indicates the complex conjugate operation on the SAR image. Represents spatial averaging, which calculates the correlation between the two images in this small area.

[0067] In this implementation case, most of the coherence coefficients in the coherence graphs obtained from 15 sets of orbital SAR data are above 0.9, such as Figure 6 As shown, the subgraph order is the same as Figure 5 Keep the order consistent.

[0068] S6: Compute the phase closure error of the pixel triplets using complex coherence.

[0069] After registering a series of SAR images, any pair of SLC images and , calculate the complex coherence coefficient between them by complex conjugate multiplication and normalization , defined as:

[0070] ;

[0071] For any triplet of three SAR images, bicoherence It is defined as the product of two complex coherences times the conjugate of a third complex coherence:

[0072] ;

[0073] in, Display image and The complex coherence coefficient, and It is the image and ,image and The complex coherence coefficient. It is still a complex number, and a higher-order coherence metric is constructed to analyze the phase closure characteristics. Its phase part is called the phase closure difference, that is, ,satisfy

[0074] ;

[0075] in, Display image and The interference phase, Display image and The interference phase, Display image and interference phase.

[0076] For any triplet of three SAR images, the bicoherence is defined as the product of two complex coherences multiplied by the conjugate of the third complex coherence:

[0077] The closure phase is the phase of the bicoherence, i.e., it is therefore a linear combination of the three interferometric phases:

[0078] In this embodiment, 15 sets of data were processed, and a total of 13 sets of pixel triplets were obtained. The closed phase was calculated according to the above formula. In this implementation case, a deeply buried rectangular leakage device was used to change the water content of the roadbed. The phase closure diagram of the multi-view ratio 3-3 is shown as follows: Figure 7 As shown in the figure, when the road surface humidity changes significantly, the phase of the area corresponding to the rectangular leakage device is not closed. Based on this, a fitting analysis is carried out on the phase closure difference and the roadbed moisture content to obtain the corresponding relationship between the two. The specific situation is as follows Figure 8As shown in the figure, the residual sum of squares is only 0.00155, indicating a low degree of deviation between the data points and the fitted line, indicating a relatively good fitting effect. Furthermore, a significant linear correlation is observed between the closure moisture content and the phase closure error, with the phase closure error increasing linearly with increasing closure moisture content.

[0079] This embodiment also provides a readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the above-mentioned method for inverting the water content of a roadbed using a multi-band track SAR device is implemented.

[0080] In addition, this embodiment also discloses a computer device, including a memory and a processor;

[0081] The memory is used to store a computer program that can be executed on the processor;

[0082] The processor is used to implement the above-mentioned steps of inverting the roadbed moisture content of the multi-band track SAR device when executing the computer program.

[0083] This embodiment uses an L-band synthetic aperture radar sensor mounted on track equipment to collect road surface data of different humidity levels, uses a back-projection algorithm to perform full-polarization SAR imaging, and performs data processing and registration, multi-view processing, interference, coherence estimation, and phase closure on the collected multi-scene SAR images. The feasibility of multi-band synthetic aperture radar full-polarization measurement technology for roadbed moisture inversion has been verified. It has the characteristics of high resolution, high flexibility, and fast response speed, providing an efficient and reliable solution for the field of roadbed moisture detection. Through data fusion, adaptive medium modeling, and efficient inversion algorithms, the adaptability bottleneck of existing technologies to complex roadbed environments has been broken through. At the same time, lightweight hardware equipment and highly compatible storage media have been developed to achieve low-cost, high-precision, and real-time monitoring of large-scale roadbed networks.

[0084] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for inverting roadbed moisture content using a multi-band track SAR device, characterized in that: The steps include: Collect road image data in different bands through orbital SAR equipment; Accurately match the positions of corresponding pixels in the primary and secondary images of the same band road image data to achieve image registration; Perform multi-view processing on the registered road image data; Perform interference processing on road surface image data to generate interference maps; Estimating the coherence of the interferogram; The phase closure difference of the pixel triplet is calculated using complex coherence, and the corresponding closure moisture content is obtained by fitting the phase closure difference.

2. The method for inverting roadbed moisture content using a multi-band track SAR device according to claim 1, characterized in that: The specific road image data of different bands collected by orbital SAR equipment are: The road surface echo signals are acquired using the HH, HV, VH, and VV polarization channels of the S-band and L-band SAR sensors to obtain road surface image data.

3. The method for inverting roadbed moisture content using a multi-band track SAR device according to claim 2, characterized in that: Accurately match the positions of corresponding pixels in the primary and secondary images of the same band road image data and achieve image registration. Specifically: Select some rows of the image as the reference area, traverse the possible translation amounts, calculate the mean square error of the two images after translation, and find the translation value that minimizes the mean square error; and according to the translation value, crop and fill zero operations on one of the images to align it with the other image in azimuth and achieve registration of the two images.

4. The method for inverting roadbed moisture content using a multi-band track SAR device according to claim 3, characterized in that: The multi-view processing of the registered road image data is as follows: In azimuth and slant range directions, Average processing and The image size after multi-view processing is the same as the original image size. .

5. The method for inverting roadbed moisture content using a multi-band track SAR device according to claim 4, characterized in that: The specific steps of interferometric processing of road surface image data are as follows: The image is divided into multiple small blocks according to the azimuth and range directions. The coherence of the two images is calculated for each small block. The interference value is obtained by complex conjugate multiplication and normalization, and an interference pattern is generated.

6. The method for inverting roadbed moisture content using a multi-band track SAR device according to claim 5, characterized in that: The coherence estimation of the interferogram is specifically: The coherence formula is used to calculate a coherence image, which is used to represent the coherence degree of different regions.

7. The method for inverting roadbed moisture content using a multi-band track SAR device according to claim 6, characterized in that: The specific calculation of the phase closure error of the pixel triplet using complex coherence is: For any triplet of three SAR images, the bicoherence is defined as the product of two complex coherences multiplied by the complex conjugate of the third, and the closed phase is the argument of the bicoherence.

8. A readable storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, a multi-band track SAR equipment roadbed moisture content inversion method according to any one of claims 1 to 7 is implemented.

9. An electronic device, characterized in that: include: At least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, a multi-band track SAR equipment roadbed moisture inversion method according to any one of claims 1 to 7.

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