CN117968631A - Pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR image - Google Patents
Pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR image Download PDFInfo
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Abstract
The invention discloses a pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR images, which relates to the technical field of ground surface deformation monitoring, and comprises the following steps: aiming at a target pavement, acquiring a satellite remote sensing image and an unmanned aerial vehicle remote sensing image, and preprocessing the images; after the image preprocessing, phase difference is carried out by utilizing a synthetic aperture radar differential interferometry technology, so as to obtain a phase difference map of pavement subsidence; disentangling the phase difference partial graph by utilizing a phase disentangling algorithm to obtain an elevation change graph of pavement subsidence; identifying and quantitatively analyzing the pavement subsidence by utilizing the elevation change graph and a threshold value judging method to obtain basic information of pavement subsidence; and performing risk assessment on the pavement subsidence by using the basic information of the pavement subsidence and the risk assessment model to obtain the risk level and the early warning signal of the pavement subsidence. The invention combines the advantages of the digital orthographic image and the satellite-borne SAR image of the unmanned aerial vehicle, and can rapidly and highly accurately realize pavement subsidence detection.
Description
Technical Field
The invention relates to the technical field of ground surface deformation monitoring, in particular to a pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR images.
Background
The surface deformation refers to the change of the position, shape or volume of the surface or underground substances under the action of external force, such as earthquake, volcano, landslide, ground crack, ground subsidence and the like. The surface deformation can cause serious threat to life, property and environmental safety of human beings, so that timely and accurately monitoring the occurrence, development and change of the surface deformation has important significance for disaster prevention and reduction.
The conventional earth surface deformation monitoring methods mainly comprise GNSS, leveling, total station, angle measurement, optical photogrammetry and the like, and the methods generally need to arrange a large number of measurement points or markers on the earth surface, and then observe periodically or continuously in a manual or automatic mode to acquire earth surface deformation data. The method has the advantages of high precision and high reliability, but also has the defects of large workload, high cost, small coverage, easiness in being influenced by weather and topography, difficulty in realizing real-time monitoring and the like.
In recent years, with the development of remote sensing technology, synthetic Aperture Radar (SAR) measurement technology is becoming an important means for monitoring surface deformation. SAR is a technology for imaging the earth surface by utilizing microwave radar signals, has the characteristics of all weather, all-day time, high resolution, high sensitivity and the like, can effectively overcome the limitations of the traditional method, and realizes the rapid, accurate and low-cost monitoring of the earth surface deformation of a large range and multiple phases. The core of the SAR measurement technology is an interferometric synthetic aperture radar (InSAR) technology, namely, two or more SAR images with a certain space baseline are utilized for interference processing, and phase difference information of the ground surface is obtained, so that the deformation of the ground surface is inverted. InSAR technology has achieved plentiful results in monitoring earth surface deformation such as earthquake, volcano, landslide, ground subsidence, etc.
However, the InSAR technique also has problems and challenges such as atmospheric delays, phase noise, loss of coherence, phase unwrapping, etc., which affect the accuracy and reliability of the InSAR results. In addition, since the SAR image can only acquire the surface deformation along the radar line-of-sight direction, the single-track SAR image is difficult to acquire the surface three-dimensional deformation, and the decomposition is required by means of the multi-track SAR image or external data or mathematical model, which increases the data amount and the calculated amount and reduces the decomposition precision and efficiency. Therefore, how to obtain the three-dimensional deformation of the earth surface by using the SAR image is still a problem to be solved.
Unmanned aerial vehicle is an aircraft capable of independently or remotely controlling flight under unmanned conditions, has the advantages of flexibility, low cost, easiness in operation and the like, and has been widely applied in various fields in recent years. Unmanned aerial vehicle photogrammetry is a technology for acquiring images and processing data on the ground surface by using sensors such as an optical camera or a laser radar carried by an unmanned aerial vehicle, and can generate high-resolution digital orthographic images (DOM), digital Elevation Models (DEM), digital Surface Models (DSM) and other products, thereby providing an effective data source for monitoring the deformation of the ground surface. The unmanned aerial vehicle photogrammetry has the advantages that the horizontal movement of the ground surface can be obtained, but the unmanned aerial vehicle photogrammetry has the defects that the vertical settlement of the ground surface is difficult to obtain, the unmanned aerial vehicle photogrammetry is limited by the flying height and the resolution of a sensor, the coverage area is small, the unmanned aerial vehicle photogrammetry is influenced by weather and topography, control points are required to be distributed on the ground surface, and the like.
Disclosure of Invention
The invention aims to provide a pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR images, which combines the advantages of unmanned aerial vehicle digital orthographic images (DOM) and satellite-borne SAR images, can rapidly and highly accurately realize pavement subsidence detection, overcomes the respective defects of SAR measurement technology and unmanned aerial vehicle photogrammetry technology, and provides a new method for monitoring ground surface deformation and geological disasters.
In order to achieve the above object, the present invention provides the following solutions:
A pavement subsidence detection method based on unmanned aerial vehicle DOM and on-board SAR images comprises the following steps:
Aiming at a target pavement, acquiring a satellite remote sensing image and an unmanned aerial vehicle remote sensing image, and preprocessing the images; the satellite remote sensing image is a synthetic aperture radar SAR image, and the unmanned aerial vehicle remote sensing image is a digital orthophoto DOM;
based on the satellite remote sensing image and the unmanned aerial vehicle remote sensing image after image preprocessing, carrying out phase difference by utilizing a synthetic aperture radar differential interferometry technology to obtain a phase difference map of pavement subsidence;
Disentangling the phase difference partial graph by utilizing a phase disentangling algorithm to obtain an elevation change graph of pavement subsidence;
identifying and quantitatively analyzing the pavement subsidence by utilizing an elevation change chart and a threshold value judging method to obtain basic information of the pavement subsidence, wherein the basic information comprises position, range and depth information;
and performing risk assessment on the pavement subsidence by using the basic information of the pavement subsidence and the risk assessment model to obtain the risk level and the early warning signal of the pavement subsidence.
Further, the image preprocessing specifically includes:
registering the satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image registration algorithm, so that the satellite remote sensing image and the unmanned aerial vehicle remote sensing image have the same coordinate system and spatial resolution;
Cutting the registered satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image cutting algorithm to obtain an image of the region of interest;
And filtering the cut satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image filtering algorithm to remove image noise and interference.
Further, the satellite remote sensing image and the unmanned aerial vehicle remote sensing image which are based on the image preprocessing perform phase difference by utilizing a synthetic aperture radar differential interferometry technology to obtain a phase difference map of pavement subsidence, and the method specifically comprises the following steps:
Carrying out interference treatment on the satellite remote sensing image subjected to image pretreatment by using a synthetic aperture radar differential interferometry technology to obtain a differential interference image;
Extracting phase change information based on the differential interferogram, wherein the phase change information represents the spatial distribution characteristics of the relative deformation of the target pavement during two imaging;
acquiring digital elevation model data based on the unmanned aerial vehicle remote sensing image after image preprocessing;
Removing phase change characteristics caused by terrain in the phase change information based on the digital elevation model data to obtain phase change characteristics caused by pavement subsidence;
And obtaining a phase difference map of the pavement subsidence based on the phase change characteristic caused by the pavement subsidence.
Further, the unwrapping the phase difference map by using a phase unwrapping algorithm to obtain an elevation change map of the pavement subsidence, specifically includes:
Converting the phase difference of each pixel point in the phase difference map into an absolute phase value by using a phase unwrapping algorithm;
converting the absolute phase value into an elevation change amount;
Mapping the elevation change amount to a corresponding geographic position to generate an elevation change map;
and carrying out smoothing and post-filtering treatment on the elevation change map.
Further, the identifying and quantitatively analyzing the pavement subsidence by using the elevation change graph and the threshold value judging method to obtain the basic information of the pavement subsidence, which comprises the following steps:
setting a threshold value, wherein the threshold value is used for distinguishing pavement subsidence and normal table elevation change;
Based on the elevation change graph, threshold value judgment is carried out on the elevation change quantity, and the area with the elevation change quantity exceeding the threshold value is identified as a potential pavement subsidence area;
Extracting features of the identified potential pavement subsidence areas, determining specific positions of pavement subsidence, namely calibrating geographic coordinate points where pavement subsidence occurs, and determining the range of the pavement subsidence areas, namely the space range of the affected surface areas; and calculating the depth of the pavement subsidence, namely the vertical distance of pavement descent, through numerical information in the elevation change chart.
Further, the risk assessment is performed on the pavement subsidence by using the basic information and the risk assessment model of the pavement subsidence to obtain a risk level and an early warning signal of the pavement subsidence, which specifically comprises the following steps:
summarizing the position, range and depth information of the pavement subsidence obtained in the previous step;
constructing a risk assessment model based on a fuzzy comprehensive evaluation method;
Determining a relevant index for evaluating the risk of pavement subsidence;
Carrying out standardized processing on the position, range, depth information and related indexes of the pavement subsidence;
Based on the risk assessment model, carrying out fuzzy comprehensive assessment on the position, range and depth information of the pavement subsidence by using a fuzzy comprehensive assessment method, and obtaining the risk level of the pavement subsidence by considering uncertainty and ambiguity;
Determining corresponding early warning signals according to the obtained risk level;
And outputting the risk level and the early warning signal.
The invention also provides a pavement subsidence detection system based on the unmanned aerial vehicle DOM and the satellite-borne SAR image, which is applied to the pavement subsidence detection method based on the unmanned aerial vehicle DOM and the satellite-borne SAR image, and comprises the following steps:
The image acquisition module comprises an image acquisition device and an image preprocessing module, wherein the image acquisition device is used for acquiring satellite remote sensing images and unmanned aerial vehicle remote sensing images, and the image preprocessing module is used for carrying out image preprocessing; the satellite remote sensing image is a synthetic aperture radar SAR image, and the unmanned aerial vehicle remote sensing image is a digital orthophoto DOM;
the phase difference module is used for carrying out phase difference by utilizing a synthetic aperture radar differential interferometry technology based on the satellite remote sensing image and the unmanned aerial vehicle remote sensing image which are subjected to image preprocessing to obtain a phase difference map of pavement subsidence;
the phase unwrapping module is used for unwrapping the phase difference partial graph by utilizing a phase unwrapping algorithm to obtain an elevation change graph of pavement subsidence;
the pavement subsidence judging module is used for identifying and quantitatively analyzing pavement subsidence by utilizing the elevation change graph and the threshold judging method to obtain basic information of pavement subsidence, wherein the basic information comprises position, range and depth information;
And the risk assessment module is used for carrying out risk assessment on the pavement subsidence by utilizing the basic information of the pavement subsidence and the risk assessment model to obtain the risk level and the early warning signal of the pavement subsidence.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the pavement subsidence detection method based on the unmanned aerial vehicle DOM and the satellite-borne SAR image, provided by the invention, the advantages of the unmanned aerial vehicle digital orthographic image (DOM) and the satellite-borne SAR image are combined based on the satellite and unmanned aerial vehicle remote sensing technology, the deformation detection of the SAR image is assisted by utilizing the high-resolution ground characteristic information provided by the DOM image, the accuracy and the reliability of the deformation detection are enhanced, and the rapid, accurate and low-cost detection and early warning of the pavement subsidence of a large range and multiple phases can be realized; the sensitivity detection of the micro change of the pavement subsidence can be realized by using the D-InSAR technology, and the detection precision and sensitivity are improved; by using the risk assessment model, the quantification of the risk level and the early warning signal of the pavement subsidence can be realized, and the effectiveness and timeliness of early warning are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow diagram of a pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR images.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR images, which utilizes satellite and unmanned aerial vehicle remote sensing technology to realize rapid pavement subsidence detection and early warning.
The synthetic aperture radar differential interferometry (DIFFERENTIAL INSAR, D-InSAR) technique is an extension of the InSAR technique, which acquires tiny deformations of the earth's surface by combining 2-shot SAR images with external terrain data. The differential interferogram is composed of SAR images acquired in two different scenes, and if the earth surface is deformed in the period of time, relevant information is recorded in the differential interferogram. The D-InSAR technology can reflect the spatial distribution characteristics of the surface deformation.
In order to overcome the defects of SAR measurement technology and unmanned aerial vehicle photogrammetry technology, the embodiment of the invention provides a pavement subsidence dynamic monitoring method and system for fusing enhanced unmanned aerial vehicle digital orthographic images (E-DOM) and multiband spaceborne Synthetic Aperture Radar (SAR) images. Compared with the prior art, the method and the device adopt the multispectral fusion technology to optimize the E-DOM, improve the spatial resolution and the color depth of the image, and combine the deep learning to perform image denoising and accurate registration. Through an improved differential interferometry (D-InSAR) technology and a machine learning algorithm, automatic calculation of three-dimensional coordinate offset of DOM homonymous pixel points is achieved, horizontal deformation and visual line deformation are integrated, and the vertical subsidence value of the earth surface is accurately calculated. The invention also introduces a risk assessment model and a dynamic threshold adjustment mechanism based on big data, and fuses real-time external data to carry out multidimensional risk assessment and early warning. The system is automatically implemented through the cloud computing platform, and continuously optimizes by combining with the field verification data, so that the prediction accuracy and the real-time response capability are improved. Compared with the prior art, the invention realizes remarkable technical innovation in the aspects of improving the quality, the automation level and the accuracy of monitoring data and the adaptability and the effectiveness of risk assessment.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the pavement subsidence detection method based on unmanned aerial vehicle DOM and satellite-borne SAR images provided by the invention at least comprises the following steps:
s1, acquiring a satellite remote sensing image and an unmanned aerial vehicle remote sensing image aiming at a target pavement, and preprocessing the images; the satellite remote sensing image is a synthetic aperture radar SAR image, and the unmanned aerial vehicle remote sensing image is a digital orthophoto DOM;
s2, carrying out phase difference by utilizing a synthetic aperture radar differential interferometry technology based on the satellite remote sensing image and the unmanned aerial vehicle remote sensing image after image preprocessing to obtain a phase difference map of pavement subsidence;
s3, unwrapping the phase difference partial graph by using a phase unwrapping algorithm to obtain an elevation change graph of pavement subsidence;
s4, identifying and quantitatively analyzing the pavement subsidence by utilizing an elevation change chart and a threshold value judging method to obtain basic information of the pavement subsidence, wherein the basic information comprises position, range and depth information;
and S5, performing risk assessment on the pavement subsidence by using the basic information of the pavement subsidence and the risk assessment model to obtain the risk level and the early warning signal of the pavement subsidence.
In the step S1, a satellite remote sensing image and an unmanned aerial vehicle remote sensing image are acquired, specifically:
Selecting a proper satellite platform (such as Sentinel-1, terra SAR-X and Beidou-X), and acquiring image data of imaging modes and data processing levels with different resolutions according to project requirements and characteristics of the satellite platform, wherein the image data comprises single-view inclined distance complex data, multi-view ground distance detection data, geocoding ellipsoid correction and enhanced ellipsoid correction.
Unmanned aerial vehicles capable of carrying payloads such as RGB, liDAR, laser scanners, thermal imaging cameras, hyperspectral and multispectral sensors are selected, flight routes are automatically planned through unmanned aerial vehicle servo algorithms, survey activities are performed to geo-reference photogrammetry projects, differential GNSS devices are used to reduce the need for ground control points, and the quality of image acquisition is ensured by means of shape self-motion algorithms.
The image preprocessing mainly comprises the steps of carrying out image registration, denoising and contrast enhancement preprocessing on SAR images and DOM images, so that the two types of images can be accurately corresponding in space. In the step S1, the image preprocessing includes registration, clipping, filtering, and the like, specifically:
registering the satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image registration algorithm, so that the satellite remote sensing image and the unmanned aerial vehicle remote sensing image have the same coordinate system and spatial resolution;
Cutting the registered satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image cutting algorithm to obtain an image of the region of interest;
And filtering the cut satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image filtering algorithm to remove image noise and interference.
The image registration algorithm comprises the following steps: selecting a reference image, wherein one of the remote sensing images is selected as the reference image, and the other images are aligned with the reference image; and then carrying out characteristic point identification: identifying common feature points such as road intersections, building corner points and the like in the reference image and the image to be registered; then the characteristic points are matched: matching the nearest neighbor with corresponding characteristic points in the two images; then estimating a transformation model: calculating a geometric transformation model according to the matched characteristic points; thereby applying transformation and resampling: and applying the transformation model to the image to be registered, and resampling by a bilinear interpolation method to finish image registration.
The image clipping algorithm is as follows: determining a clipping region: determining geographic coordinates or pixel coordinates of a region of interest (ROI) according to the study requirements; then setting clipping parameters: setting the size and the position of a clipping region in image processing software; and then performs a clipping operation: performing a clipping operation using GDAL and the PIL library in Python; finally, the clipping result is saved: and saving the cut image as a new file so as to facilitate subsequent analysis.
The image filtering algorithm is as follows: selecting a filter type: selecting a Gaussian filter according to the noise type and the processing target; then setting filtering parameters: determining a kernel size and shape of the filter, and then applying the filter on the image; the filtering effect is then checked: observing images before and after filtering to ensure that noise is effectively removed, and simultaneously, retaining important image characteristics; and finally, saving a filtering result: and saving the filtered image as a new file for use in subsequent steps.
The invention extracts the phase difference information of the pavement subsidence from the SAR image based on the SAR image and the DOM image, and detects the micro deformation of the ground by utilizing the interferometric technique (InSAR) of the SAR image. At the same time, ground features such as edges and contours of roads, buildings are extracted from the DOM images.
In the step S2, based on the satellite remote sensing image and the unmanned aerial vehicle remote sensing image after the image preprocessing, the phase difference is performed by using the synthetic aperture radar differential interferometry technology, so as to obtain a phase difference map of the pavement subsidence, which specifically comprises:
Carrying out interference treatment on the satellite remote sensing image subjected to image pretreatment by using a synthetic aperture radar differential interferometry technology to obtain a differential interference image;
Extracting phase change information based on the differential interferogram, wherein the phase change information represents the spatial distribution characteristics of the relative deformation of the target pavement during two imaging;
acquiring digital elevation model data based on the unmanned aerial vehicle remote sensing image after image preprocessing;
Removing phase change characteristics caused by terrain in the phase change information based on the digital elevation model data to obtain phase change characteristics caused by pavement subsidence;
And obtaining a phase difference map of the pavement subsidence based on the phase change characteristic caused by the pavement subsidence.
The phase difference map obtaining step comprises the following steps:
s201, performing interference processing on a satellite remote sensing image by utilizing an SAR interference imaging principle to obtain an interference pattern and a phase diagram;
s202, carrying out flat-top phase elimination on the phase map by using DEM auxiliary information to obtain a flat-top phase map;
and S203, performing differential processing on the flat-top phase map by using the multi-phase SAR image to obtain a phase difference map.
Specifically, a SAR image pair is first selected: selecting two SAR images which are close in time and cover the same area, and generating an interference image by the two images; registration of the image pair then: accurately registering the selected SAR image pair to ensure that pixels of the two images are completely aligned; further, interference processing software is used for generating an interference pattern by calculating the difference of phase differences of the two images; next, phase difference is performed: extracting phase information from the interferograms, the phase information representing the relative motion of the ground object during the two imaging; then eliminating the flat-top phase, and removing phase change caused by terrain by using Digital Elevation Model (DEM) data so as to focus on the phase change caused by ground subsidence; then phase filtering: filtering the extracted phase difference data to reduce noise and improve the quality of the phase data; phase unwrapping: performing phase unwrapping, converting the phase difference of each pixel point into an absolute phase difference, which is a key step in calculating the ground subsidence amount; calculating ground settlement: and converting the disentangled phase difference into ground settlement. This typically involves converting the phase difference into units of length (e.g., millimeters) to generate a phase difference profile: mapping the calculated ground settlement amount to a corresponding geographic position to generate a phase difference map representing ground settlement; and finally, quality control and verification are carried out: and performing quality control on the generated phase difference map, checking whether any abnormality or inconsistency exists, and comparing and verifying with ground observation data.
Among them, in Synthetic Aperture Radar (SAR) interferometry, a topography-induced phase change refers to a change in the phase of a radar signal caused by natural fluctuations and height differences of the ground. When SAR signals are transmitted from and reflected back to the satellite, the different heights and topographical features of the ground affect the propagation path length of the signals, resulting in variations in the phase of the received signals. This phase change caused by the topography relief appears in an interference pattern (interferogram) as a series of fringes or patterns that represent a changing information of the elevation of the ground.
The use of Digital Elevation Model (DEM) data to remove terrain-induced phase variations is an important step in the synthetic aperture radar differential interferometry (DInSAR) technique. The specific operation steps are as follows:
Obtaining DEM data: first, high-precision DEM data covering an area to be measured needs to be acquired. The DEM is a digital model for recording the ground elevation information in detail, and can reflect the relief situation of the terrain.
Simulating terrain phase: using DEM data, the theoretical phase change due to terrain relief without any other deformation (such as ground subsidence) can be simulated. This process typically involves complex terrain radiation models and algorithms to calculate the expected phase change for a given terrain condition.
Phase subtraction: the simulated terrain phase is subtracted from the actual observed interference phase to remove the effect of the terrain-induced phase change. Thus, the remaining phase changes reflect more of the deformation caused by non-topographical factors such as ground subsidence.
After the removal of the topography phase, the remaining phase change in the interferogram is mainly caused by deformation such as ground subsidence, which enables researchers to more accurately analyze and quantify the degree and extent of ground subsidence.
Through the steps, researchers can effectively distinguish and remove the topographic effect and focus on researching the phase change caused by the surface deformation such as ground subsidence, thereby improving the accuracy and the reliability of deformation monitoring.
In the step S3, the phase unwrapping algorithm is a least square method-based phase unwrapping algorithm, and the principle is as follows: the phase unwrapping problem is converted into a least square optimization problem, and an elevation change map of the pavement subsidence is obtained by solving a linear equation set.
Specifically, the unwrapping the phase difference map by using a phase unwrapping algorithm to obtain an elevation change map of the pavement subsidence includes:
Firstly, selecting a branch cutting algorithm to perform a phase unwrapping algorithm according to data characteristics and unwrapping requirements; importing the image subjected to the phase difference processing into unwrapping software or a tool; setting an unwrapping parameter: setting key parameters in unwrapping processes such as iteration times, filter types, region division and the like according to the selected algorithm and specific data characteristics; then, an unwrapping algorithm is operated, and the phase difference of each pixel point in the phase difference diagram is converted into an absolute phase value; and continuously converting the unwrapped phase value into the elevation change quantity. Mapping the calculated elevation change amount to a corresponding geographic position to generate an elevation change map; further, smoothing and filtering post-processing are carried out on the elevation change graph, so that the usability and the visual quality of the data are improved; continuing to compare with the field measurement data or other independent data sources to verify and correct the accuracy of the elevation change map; and finally, outputting a final result, and outputting the processed elevation change graph into a format suitable for further analysis and display.
In the step S4, the pavement subsidence is identified and quantitatively analyzed by using an elevation change chart and a threshold value judgment method to obtain the basic information of pavement subsidence, which specifically comprises the following steps:
Firstly, an elevation change chart obtained by a phase unwrapping algorithm is utilized, and the elevation change condition of pavement subsidence is reflected through the chart; then determining a threshold value: setting a threshold value for distinguishing pavement subsidence and normal surface elevation change, wherein the selection of the threshold value can be adjusted according to specific conditions and project requirements; then, threshold value judgment is carried out on the elevation change map, and the area with the elevation change value exceeding the set threshold value is marked as a potential pavement subsidence area; further extracting the subsidence features, and extracting the features of the part marked as the potential subsidence area, wherein the features comprise information such as position, range, depth and the like;
Further identifying the pavement subsidence position, and determining the specific position of pavement subsidence according to the result of the threshold value judging method, namely identifying the geographic coordinate point of pavement subsidence in the image, further measuring the pavement subsidence range, and determining the range of the pavement subsidence area, namely the space range of the affected surface area; and finally, calculating the pavement subsidence depth, namely the vertical distance of pavement descent, through numerical information in the elevation change map.
The determining process of the information such as the position, the range, the depth and the like specifically comprises the following steps:
(1) Elevation change map analysis: firstly, obtaining an elevation change graph from interference SAR data through a phase unwrapping algorithm, wherein the elevation change graph shows the change of the elevation of the ground surface during observation; phase unwrapping is a process of converting an interference phase into an absolute phase difference that can directly reflect the elevation change.
(2) Threshold judgment: then, a threshold value is set to distinguish normal ground surface elevation change from abnormal elevation change caused by subsidence; regions exceeding this threshold are considered potential subsidence areas; the selection of the threshold depends on the surface conditions, the length of observation time, and the specific requirements of the project.
(3) Potential subsidence area identification: by comparing the change value of each point in the elevation change map to a threshold value, areas where the change value exceeds the threshold value are marked as potential subsidence areas.
(4) Feature extraction: for marked potential subsidence areas, further analyzing the geographic location and extent of these areas; this may be achieved by image processing and analysis algorithms such as edge detection, region growing or clustering algorithms.
(5) Depth determination: the determination of depth depends on unwrapped phase information and SAR system parameters, in particular:
the phase difference is converted into elevation change: the unwrapped phase difference (typically expressed in radians) can be converted to elevation change by the following formula ）：
Wherein,Is the phase difference after unwrapping,/>Is the radar wavelength,/>Is the incident angle,/>Is the amount of elevation change along the line of sight;
Estimating the subsidence depth: the depth of the subsidence area can be further estimated through the altitude change chart obtained through calculation; it should be noted that the depth here refers to the variation along the radar line of sight, and may need to be adjusted according to the ground inclination angle or other geographical information to obtain a true depth value perpendicular to the ground.
The determination process of the information such as the position, the range, the depth and the like relates to the comprehensive application of remote sensing data processing, image analysis and Geographic Information System (GIS) technology. The specific algorithms and computational steps may be adapted according to the actual situation and available data. In practical applications, these steps typically require specialized software and libraries of algorithms to implement.
In the step S5, risk assessment is performed on the pavement subsidence by using the basic information and the risk assessment model of the pavement subsidence to obtain a risk level and an early warning signal of the pavement subsidence, which specifically comprises:
S501, firstly, summarizing key information such as the position, the range, the depth and the like of the pavement subsidence obtained in the previous step; then, a risk assessment model is established, and a risk assessment model based on a fuzzy comprehensive evaluation method is designed and established, wherein the model considers different influence factors of pavement subsidence and weight relations of the factors;
the risk assessment model of the pavement subsidence is as follows:
in the method, in the process of the invention, Representing a final risk assessment result; /(I)Is a comprehensive evaluation function; /(I)Is an evaluation index weight set and can be expressed as/>Each/>Weights corresponding to a particular evaluation index (e.g., location, range, depth, etc.); /(I)A fuzzy evaluation matrix which is an evaluation index can be expressed as/>Wherein/>Represents the/>The number of evaluation objects is at the/>Membership or scoring under the individual evaluation indicators. Final risk assessment results/>The method can be calculated by the following formula:
Here, the The synthesis operation representing a fuzzy matrix generally involves multiplication and maximization and minimization operations of the matrix; for example, if a common method in fuzzy comprehensive evaluation is adopted, then:
Wherein, Is directed to (I)Comprehensive evaluation results of individual evaluation grades,/>Representing the maximum value (maximization operation),Representing taking the minimum value (minimization operation) for modeling the logical relationship of "and";
And comprehensively considering different influencing factors and weights of the pavement subsidence to obtain a quantized risk assessment result, so as to help further analysis and decision.
S502, determining specific indexes for evaluating the pavement subsidence risk, wherein the indexes can comprise factors affecting personnel, traffic, environment and the like; then, data standardization is carried out, and the collected pavement subsidence information and related indexes are subjected to standardization treatment so as to carry out comprehensive evaluation in the model; and further performing fuzzy evaluation, performing fuzzy comprehensive evaluation on the standardized data by using a fuzzy comprehensive evaluation method, and obtaining the risk level of pavement subsidence by considering uncertainty and ambiguity.
Specific indicators for assessing risk of road subsidence generally include, but are not limited to:
depth (D): the maximum depth of subsidence, typically in meters;
range (R): the area of the subsidence area, typically in square meters;
Influence person (P): the number of people affected;
Traffic impact (T): influence on traffic flow and road availability;
Environmental impact (E): potential impact on the surrounding environment and ecosystem.
Specifically, the data normalization includes: all indexes are firstly converted into values in the interval of [0, 1] through standardization processing so as to carry out comprehensive evaluation in a model; the normalization method may be a max-min normalization, a Z-score normalization, etc.
Further, risk classification includes: assuming that each evaluation object obtains a composite score between 0 and 1 after fuzzy composite evaluation; based on this score, the risk level can be broadly divided into the following categories:
very low (0-0.2): almost no influence is caused, and no measures are required;
Low (0.2-0.4): less impact, monitoring and few precautions may be required;
medium (0.4-0.6): has obvious influence, and needs to be evaluated and intervened to a medium degree;
high (0.6-0.8): the influence is serious, and immediate and effective measures need to be taken;
very high (0.8-1.0): extremely serious influences, emergency measures must be carried out immediately.
S503, continuing to formulate early warning signals, and determining corresponding early warning signals according to the obtained risk level, wherein the early warning signals can comprise different colors, levels or other marks so as to send clear early warning information to related parties when needed; and finally, outputting risk level and early warning signals: and outputting the risk level of pavement subsidence and corresponding early warning signals so as to enable a decision maker, a manager or related personnel to take necessary actions in time.
The risk assessment model is based on a fuzzy comprehensive judgment method, and the principle is as follows: and determining influence factors and weights of the pavement subsidence according to the information of the pavement subsidence such as the position, the range and the depth, constructing a fuzzy judgment matrix, and calculating the risk level and the early warning signal of the pavement subsidence by using a fuzzy comprehensive judgment method.
Based on the framework of the index and the risk level, an exemplary fuzzy judgment matrix can be constructed. This matrix is intended to be used for risk assessment in connection with various evaluation indicators (depth, scope, influencing personnel, traffic influence, environmental influence) and risk levels (very low, medium, high, very high).
Let us assume that we define the evaluation index set asAnd risk level set as/>= { Very low, medium, high, very high }, membership of corresponding risk classes under different evaluation indexes is shown in table 1: TABLE 1
The columns are: representing different risk levels, from "very low" to "very high".
Row: representing different evaluation indexes.
Values in cells: representing the membership or likelihood of the corresponding risk level for a given indicator. For example, a degree of membership of depth (D) to a "medium" risk level of 0.4 means that the depth indicator contributes or is likely to be greater to determine that a situation is a "medium" risk.
When the fuzzy evaluation matrix is used, the weight of each index is required to be determined, then the comprehensive membership degree of each risk level is calculated by using the fuzzy evaluation matrix and the weight through a fuzzy comprehensive evaluation method, and finally the risk level with the largest membership degree is selected as a final evaluation result.
The invention also provides a pavement subsidence detection system based on the enhanced unmanned aerial vehicle digital orthophoto (E-DOM) and the satellite-borne SAR image, which comprises the following steps:
The image acquisition module comprises an image acquisition device and an image preprocessing module, wherein the image acquisition device is used for acquiring satellite remote sensing images and unmanned aerial vehicle remote sensing images, and the image preprocessing module is used for carrying out image preprocessing; the satellite remote sensing image is a synthetic aperture radar SAR image, and the unmanned aerial vehicle remote sensing image is a digital orthophoto DOM;
the phase difference module is used for carrying out phase difference by utilizing a synthetic aperture radar differential interferometry technology based on the satellite remote sensing image and the unmanned aerial vehicle remote sensing image which are subjected to image preprocessing to obtain a phase difference map of pavement subsidence;
the phase unwrapping module is used for unwrapping the phase difference partial graph by utilizing a phase unwrapping algorithm to obtain an elevation change graph of pavement subsidence;
the pavement subsidence judging module is used for identifying and quantitatively analyzing pavement subsidence by utilizing the elevation change graph and the threshold judging method to obtain basic information of pavement subsidence, wherein the basic information comprises position, range and depth information;
And the risk assessment module is used for carrying out risk assessment on the pavement subsidence by utilizing the basic information of the pavement subsidence and the risk assessment model to obtain the risk level and the early warning signal of the pavement subsidence.
In conclusion, the method utilizes the advantages of satellite and unmanned aerial vehicle remote sensing images, and realizes multi-level and multi-time phase detection and early warning of pavement subsidence; the invention utilizes the differential interferometry (D-InSAR) technology of the synthetic aperture radar, realizes the sensitive detection of the tiny change of the pavement subsidence, improves the detection precision and sensitivity, and avoids the problem that only the point cloud data of the laser radar is utilized and some tiny subsidence phenomenon can be ignored; the risk assessment model based on the fuzzy comprehensive assessment method is utilized, the quantification of the risk level and the early warning signal of the road surface subsidence is realized, and the effectiveness and timeliness of early warning are improved.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. The pavement subsidence detection method based on the unmanned aerial vehicle DOM and the satellite-borne SAR image is characterized by comprising the following steps of:
Aiming at a target pavement, acquiring a satellite remote sensing image and an unmanned aerial vehicle remote sensing image, and preprocessing the images; the satellite remote sensing image is a synthetic aperture radar SAR image, and the unmanned aerial vehicle remote sensing image is a digital orthophoto DOM;
based on the satellite remote sensing image and the unmanned aerial vehicle remote sensing image after image preprocessing, carrying out phase difference by utilizing a synthetic aperture radar differential interferometry technology to obtain a phase difference map of pavement subsidence;
Disentangling the phase difference partial graph by utilizing a phase disentangling algorithm to obtain an elevation change graph of pavement subsidence;
identifying and quantitatively analyzing the pavement subsidence by utilizing an elevation change chart and a threshold value judging method to obtain basic information of the pavement subsidence, wherein the basic information comprises position, range and depth information;
and performing risk assessment on the pavement subsidence by using the basic information of the pavement subsidence and the risk assessment model to obtain the risk level and the early warning signal of the pavement subsidence.
2. The pavement subsidence detection method based on the unmanned aerial vehicle DOM and the spaceborne SAR image according to claim 1, wherein the image preprocessing specifically comprises:
registering the satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image registration algorithm, so that the satellite remote sensing image and the unmanned aerial vehicle remote sensing image have the same coordinate system and spatial resolution;
Cutting the registered satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image cutting algorithm to obtain an image of the region of interest;
And filtering the cut satellite remote sensing image and the unmanned aerial vehicle remote sensing image by using an image filtering algorithm to remove image noise and interference.
3. The method for detecting the pavement subsidence based on the unmanned aerial vehicle DOM and the satellite-borne SAR image according to claim 1, wherein the satellite remote sensing image and the unmanned aerial vehicle remote sensing image which are subjected to image preprocessing are subjected to phase difference by utilizing a synthetic aperture radar differential interferometry technology to obtain a phase difference map of the pavement subsidence, and the method specifically comprises the following steps:
Carrying out interference treatment on the satellite remote sensing image subjected to image pretreatment by using a synthetic aperture radar differential interferometry technology to obtain a differential interference image;
Extracting phase change information based on the differential interferogram, wherein the phase change information represents the spatial distribution characteristics of the relative deformation of the target pavement during two imaging;
acquiring digital elevation model data based on the unmanned aerial vehicle remote sensing image after image preprocessing;
Removing phase change characteristics caused by terrain in the phase change information based on the digital elevation model data to obtain phase change characteristics caused by pavement subsidence;
And obtaining a phase difference map of the pavement subsidence based on the phase change characteristic caused by the pavement subsidence.
4. The method for detecting the pavement subsidence based on the unmanned aerial vehicle DOM and the spaceborne SAR image according to claim 1, wherein the phase difference map is unwrapped by using a phase unwrapping algorithm to obtain an elevation change map of the pavement subsidence, specifically comprising:
Converting the phase difference of each pixel point in the phase difference map into an absolute phase value by using a phase unwrapping algorithm;
converting the absolute phase value into an elevation change amount;
Mapping the elevation change amount to a corresponding geographic position to generate an elevation change map;
and carrying out smoothing and post-filtering treatment on the elevation change map.
5. The method for detecting the pavement subsidence based on the unmanned aerial vehicle DOM and the spaceborne SAR image according to claim 1, wherein the method for identifying and quantitatively analyzing the pavement subsidence by using the elevation change map and the threshold value judgment method is characterized by comprising the following steps:
setting a threshold value, wherein the threshold value is used for distinguishing pavement subsidence and normal table elevation change;
Based on the elevation change graph, threshold value judgment is carried out on the elevation change quantity, and the area with the elevation change quantity exceeding the threshold value is identified as a potential pavement subsidence area;
Extracting features of the identified potential pavement subsidence areas, determining specific positions of pavement subsidence, namely calibrating geographic coordinate points where pavement subsidence occurs, and determining the range of the pavement subsidence areas, namely the space range of the affected surface areas; and calculating the depth of the pavement subsidence, namely the vertical distance of pavement descent, through numerical information in the elevation change chart.
6. The method for detecting the pavement subsidence based on the unmanned aerial vehicle DOM and the spaceborne SAR image according to claim 1, wherein the risk assessment is carried out on the pavement subsidence by using the basic information and the risk assessment model of the pavement subsidence to obtain the risk level and the early warning signal of the pavement subsidence, and the method specifically comprises the following steps:
summarizing the position, range and depth information of the pavement subsidence obtained in the previous step;
constructing a risk assessment model based on a fuzzy comprehensive evaluation method;
Determining a relevant index for evaluating the risk of pavement subsidence;
Carrying out standardized processing on the position, range, depth information and related indexes of the pavement subsidence;
Based on the risk assessment model, carrying out fuzzy comprehensive assessment on the position, range and depth information of the pavement subsidence by using a fuzzy comprehensive assessment method, and obtaining the risk level of the pavement subsidence by considering uncertainty and ambiguity;
Determining corresponding early warning signals according to the obtained risk level;
And outputting the risk level and the early warning signal.
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