67 results about "Interferometric synthetic aperture radar" patented technology

Disclosed is a complex scene deformation monitoring and classifying method based on interferometric synthetic aperture radar (InSAR) and a deep learning self-attention model, including: generating a deformation interferogram by using an InSAR extraction technology combining persistent scatterer (PS) points and distributed scatter (DS) points and relying on a shuttle radar topography mission digital elevation model (SRTM DEM) image; constructing a Delaunay triangulation network and an annular zone, obtaining a time series deformation data of PS points and DS points through adaptive arc densification and omni-directional point expansion, and extracting settlement points on a bridge; decomposing a time series by an SAR-self-attention model, decomposing InSAR time series data into a trend component and a seasonal component, and interpreting and analyzing deformation of the sea-crossing bridge; and describing time series dynamics and a seasonal pattern by comparing a curve fitting method with a seasonal and trend decomposition method.

The invention discloses a reservoir bank slope deformation monitoring and early warning processing method based on InSAR data, and relates to the technical field of reservoir bank slope deformation monitoring, and the method specifically comprises the following steps: obtaining slope deformation characteristic information influenced by sudden drop of a water level in real time under the condition that the water level of a reservoir is determined to drop sharply in a short time, and carrying out the analysis after obtaining, respectively generating a water level sudden drop deformation response coefficient and an instantaneous instability trend index; constructing an instantaneous instability risk assessment model for the generated water level sudden drop deformation response coefficient and the instantaneous instability trend index, and generating a slope instantaneous instability assessment coefficient; and performing evaluation analysis based on the slope instantaneous instability evaluation coefficient, and evaluating the slope instantaneous instability risk under the condition that the water level of the reservoir drastically drops within a short time. According to the method, the problem that the instantaneous instability risk of the side slope cannot be evaluated under the condition that the water level of the reservoir drops sharply within a short time in the existing InSAR monitoring technology is solved, and dynamic monitoring and early warning with high timeliness and accurate quantification are realized.

The invention discloses a dam deformation monitoring method based on multi-source heterogeneous data fusion, and relates to the technical field of deformation monitoring. The method comprises the following steps: collecting dam deformation data containing remote sensing deformation data and ground monitoring data; extracting remote sensing deformation data features by using an interferometric synthetic aperture radar method; extracting ground monitoring data features and correcting the ground monitoring data features by using a multi-rate Kalman filtering method to obtain monitoring deformation correction data; carrying out difference analysis and processing on the two types of correction and feature data, and weighting through an entropy weight method to obtain dam deformation fusion data; constructing a space-time coupling prediction model, and inputting fusion data to obtain a space-time deformation prediction tensor; and calculating time and space risk components according to the prediction tensor, and combining to obtain a space-time coupling risk index for dam deformation monitoring. According to the method, a fusion algorithm of multi-source heterogeneous data is creatively adopted, the integrity of dam deformation monitoring is guaranteed, and the efficiency of dam disaster prevention and emergency response is greatly improved.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The invention provides a mining area deformation monitoring and risk assessment method, which comprises the following steps of: carrying out registration, de-leveling, filtering and phase unwrapping on acquired image data through InSAR (Interferometric Synthetic Aperture Radar), generating a coherence graph, a filtering interference graph and an unwrapping phase graph, carrying out phase decomposition by using a singular value algorithm, separating a deformation phase, an atmospheric delay phase and noise, and carrying out risk assessment. The method comprises the steps of expanding discrete deformation points into a continuous deformation field through Kriging interpolation, carrying out deformation grading by taking a deformation rate, an accumulated settlement amount and a deformation gradient as core indexes, identifying a deformation high-risk area, arranging measurement robot monitoring points in the high-risk area, and constructing a risk assessment model in combination with real-time deformation data of a measurement robot and geological parameters. And evaluating the risk grade of each part of the mining area through the dynamic risk index model, and triggering graded early warning. According to the method, the InSAR and measurement robot technology is combined to solve the data fusion problem of large-range deformation monitoring and local high-precision monitoring, and the dynamic risk assessment precision under the complex terrain of the mining area is improved.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) deformation inversion method for lifting rail offset correction, and mainly relates to the technical field of surface deformation monitoring. In order to solve the problem that the deformation monitoring quantity of the existing multi-dimensional small baseline set formed aperture radar interference is obviously lower than the actual deformation quantity, a time sequence deformation observation equation is established by using multi-orbit synthetic aperture radar data, an ascending-orbit small baseline set InSAR settlement monitoring point cloud and a descending-orbit small baseline set InSAR settlement monitoring point cloud are calculated respectively, point cloud registration is carried out, and the settlement monitoring point cloud of the ascending-orbit small baseline set InSAR settlement monitoring point cloud is calculated to obtain the settlement monitoring point cloud of the descending-orbit small baseline set InSAR settlement monitoring point cloud. Calculating the offset of the monitoring result of the lifting rail in the vertical, east-west and south-north directions; calculating to obtain an ascending orbit incidence angle correction number and a descending orbit incidence angle correction number of the synthetic aperture radar image by using the offsets of the ascending orbit monitoring result in the vertical direction, the east-west direction and the south-north direction; and finally, constructing a multi-dimensional small baseline set InSAR deformation inversion model containing lifting rail offset correction, solving to obtain a corrected deformation monitoring quantity, and improving the deformation monitoring precision.

The present invention relates to the technical field of prevention and control of geological disasters in mountainous coal mining areas. Disclosed are an InSAR-based measurement method for the angle of critical deformation of a coal mining area, and a system. The method comprises: acquiring a mining-induced surface deformation field image by means of small baseline subset-interferometric synthetic aperture radar; in the mining-induced surface deformation field image, separately drawing profile lines along a coal seam strike and a coal seam dip, and selecting target deformation points on the profile lines as points to be monitored; according to the incident angle, the flight direction and the azimuth angle of a satellite, obtaining vertical and horizontal deformation values; using a probability integral method to calculate parameters such as inclination, curvature and horizontal deformation; and drawing a curve graph on the basis of the vertical deformation of a target region and the above parameters, so as to obtain a corresponding angle of critical deformation. The InSAR-based measurement method for angle of critical deformation of a coal mining area provided by the present invention has the advantages of improved temporal resolution and reduced phase noises and the like, can achieve high precision and high efficiency, and can generate clear and visible Excel tables for surface deformation data of mining areas and corresponding profile data.

The present invention discloses a method and system for evaluating post-earthquake damage of building groups based on interferometric synthetic aperture radar (InSAR). Method includes: determining an investigation region, acquiring pre- and post-earthquake radar images of a corresponding district, and acquiring radar datasets included in radar images; acquiring a radar line-of-sight (LOS)-direction deformation map of investigation region by processing pre- and post-earthquake radar images using D-InSAR; acquiring a deformation in a main earthquake direction by decomposing an LOS-direction deformation; drawing a deformation classification map of buildings; acquiring pre- and post-earthquake optical remote sensing images of investigation region, and locating post-earthquake collapsed buildings; locating collapsed buildings in deformation classification map, comparing optical remote sensing images with deformation classification map, and locating exact positions of collapsed buildings; and evaluating post-earthquake damage of the buildings by evaluating post-earthquake damage conditions based on position information of collapsed buildings and the deformation classification map.

The present invention discloses a method and system for evaluating post-earthquake damage of building groups based on interferometric synthetic aperture radar (InSAR). Method includes: determining an investigation region, acquiring pre- and post-earthquake radar images of a corresponding district, and acquiring radar datasets included in radar images; acquiring a radar line-of-sight (LOS)-direction deformation map of investigation region by processing pre- and post-earthquake radar images using D-InSAR; acquiring a deformation in a main earthquake direction by decomposing an LOS-direction deformation; drawing a deformation classification map of buildings; acquiring pre- and post-earthquake optical remote sensing images of investigation region, and locating post-earthquake collapsed buildings; locating collapsed buildings in deformation classification map, comparing optical remote sensing images with deformation classification map, and locating exact positions of collapsed buildings; and evaluating post-earthquake damage of the buildings by evaluating post-earthquake damage conditions based on position information of collapsed buildings and the deformation classification map.

The invention relates to an earth surface deformation detection and classification method and system fusing CNN and SBAS-InSAR technologies, and the method comprises the steps: obtaining high-precision deformation data through a small baseline subset and an interference synthesis radar technology (SBAS-InSAR), constructing a CNN classification frame through combining PCA-enhanced optical satellite images, elevation, soil physical parameters, geological landform maps and other multi-source geographic information, and carrying out the detection and classification of the earth surface deformation through the CNN classification frame and the SBAS-InSAR technology. And identification of deformation types such as landslide, settlement and lifting is realized. The process comprises multi-source satellite data acquisition and preprocessing (including InSAR sight velocity decomposition and optical / topographic feature extraction), generation of a preliminary deformation label based on a classification algorithm of a gradient and a deformation velocity threshold, and recognition of a deformation type through a multilayer CNN model. According to the method, the deformation mode is automatically clustered and analyzed in a large-range area, the accuracy and detail performance of deformation type recognition are remarkably improved, the accuracy rate reaches 93%, the method is superior to a traditional method, a stable and extensible tool platform is provided for geological disaster monitoring, and positive risk management of landslide or land subsidence prone areas is assisted.

The invention relates to the technical field of slope reinforcement, and particularly discloses a slope reinforcement system and method for building municipal administration, slope rock mass strain time sequence data is collected through a distributed optical fiber sensing system, slope deformation monitoring data is obtained through a spaceborne interferometric synthetic aperture radar, and a three-dimensional deformation monitoring data set is constructed; an LSTM-rock constitutive model fusing a DFOS strain field and an InSAR deformation field is established, strain time sequence data and deformation data are input into the model for three-dimensional stress field inversion, and shear modulus degradation rate parameters, creep coefficient parameters and displacement increment prediction data are output; shear modulus degradation rate parameters, creep coefficient parameters and displacement increment prediction data are processed, regulation and control strategy codes of anchor rod tensioning force gradients and grouting pressure intervals are generated and uploaded to a control terminal, the precision and timeliness of slope stability analysis are remarkably improved, accurate intervention can be conducted in time according to potential risks of a slope, and the stability of the slope is improved. The probability of geological disasters such as landslide is reduced, and life and property safety of people is protected.

The invention belongs to the technical field of ocean surface flow velocity remote sensing observation, and particularly relates to a wave deviation correction method based on wave orbit motion and scattering characteristic analysis, which comprises the following steps of: establishing a wave deviation correction method driven by a physical mechanism in combination with wave orbit motion and Bragg wave scattering intensity change; generating simulation data through the M4S model to train the correction model; applying the trained model to actually measured data of an airborne Ka-band along-track interferometric synthetic aperture radar of the Jones Zhou straits of the South China Sea, and verifying the effectiveness of the model by comparing and analyzing the actually measured data with real data provided by a GNSS drifting buoy; the reduction amplitude of the radial surface flow velocity measurement error is calculated, and the experimental result shows that the error is averagely reduced by about 83.5%. The invention provides an efficient and innovative technical scheme for improving the remote sensing measurement accuracy of the ocean surface flow velocity, can obviously reduce the remote sensing measurement error of the ocean surface flow velocity, and is suitable for the application fields of ocean environment monitoring, climate change research and ocean engineering.

A computerized method estimates crop growth in a geographic area using satellite-collected synthetic aperture radar (SAR) data. SAR data of the geographic area is obtained from a plurality of satellite passes by one or more satellites. The obtained SAR data is processed into coherence data and interferometric data. The processed data is associated with a comparison between a first SAR data subset from a first satellite pass of the plurality of satellites passes and a second SAR data subset from a second satellite pass of the plurality of satellite passes. The processed data is provided to a trained crop growth estimation model and a crop growth prediction associated with the geographic area is generated using the trained crop growth estimation model. In some examples, the obtained SAR data is processed into additional data types, such as amplitude data and / or polarimetric SAR data.

The invention discloses a time sequence InSAR ionosphere error robust correction method based on Re-RSSI, and the method comprises the steps: estimating a space-time baseline of each SAR image pair, determining an SAR image pair set needed by time sequence solution according to a preset space-time baseline threshold value, carrying out the band-pass filtering of an original SLC image, carrying out the registration of the full-bandwidth and sub-band SLC image set, and carrying out the interference processing. The method comprises the following steps: obtaining a full-bandwidth and high-low-frequency double-difference interferogram, carrying out phase unwrapping, obtaining a full-bandwidth and high-low-frequency double-difference unwrapping phase, carrying out filtering processing, obtaining a filtering unwrapping interferogram, estimating the full-bandwidth and high-low-frequency double-difference time sequence ionosphere phase in combination with a weighted least square algorithm, and removing an estimation result from an original InSAR (Interferometric Synthetic Aperture Radar). And ionization error correction is completed. According to the method, the ionosphere error correction capability is effectively improved, the influence of the ionosphere error on the monitoring precision is reduced, and the high precision of the geological disaster monitoring result is ensured.

The invention discloses an InSAR relative phase gradient-combined seismic fracture process inversion method, which comprises the following steps of: performing gradient operation on an InSAR phase difference to obtain an InSAR relative gradient; the relative gradient serves as observation data, the minimum residual error of the relative gradient serves as a target function, the curvature and radian of the interference fringes are restrained, and therefore seismic co-seismic sliding distribution inversion based on the interference fringes which cannot be unwound on the dive zone near-field island is achieved. By adopting the technical scheme of the invention, the inversion precision of the same-seismic sliding distribution of the subduction zone earthquake under the observation constraint of the unconstrained InSAR is improved.

The invention provides a space-ground integrated rural road disease monitoring method based on SBAS-InSAR, and relates to the technical field of road engineering monitoring, small baseline set interferometric synthetic aperture radar processing is carried out on acquired synthetic aperture radar time sequence image data, depth decomposition is carried out on a time sequence displacement sequence, and an SBAS-InSAR image is obtained; a thermoelastic phase component highly related to meteorological temperature data is separated out, abnormal monitoring of road state attributes is carried out, and early warning can be carried out at the stage that macroscopic cracks or measurable macroscopic settlement do not occur on a road. A three-dimensional profile model is constructed, and a graph attention network is introduced to analyze and process an attribute heterogeneous graph covering road information, so that a complex cross-structure layer causal association rule of a road surface can be automatically learned and established, and a quantitative structural risk score and an association graph for revealing contribution degrees of each feature are output; comprehensive structure early warning of the road is carried out, and comprehensive diagnosis, cause inference and double risk early warning of the health condition of the rural road structure are realized.

The invention discloses a soft soil area earth surface deformation identification method and system based on Beidou short message and InSAR cooperation, and the method comprises the steps: collecting high-frequency deformation data through a ground sensor array, obtaining a low-frequency wide-area deformation image through combining with an InSAR satellite, and constructing a multi-source data set through space-time matching; and data missing is filled by adopting an interpolation algorithm, and the data quality is ensured through consistency verification. And a support vector machine classifier is used to identify an abnormal settlement area, and a filtering algorithm is used to predict a deformation trend based on a historical sequence. And when the predicted value exceeds a threshold value, an alarm signal is automatically generated, a thermodynamic diagram is visually rendered through a GIS system, and the high-risk infrastructure is positioned. And finally, the data set is circularly updated through real-time sensor feedback, fusion parameters are iteratively optimized, and hidden deformation dynamic accurate sensing is realized. According to the method, the hidden deformation identification precision is remarkably improved, and efficient and accurate monitoring and early warning support is provided for the safety of infrastructures in the soft soil area.

The invention discloses a landslide emergency monitoring system, and the system comprises a ground-based interference synthetic aperture radar device which is used for monitoring the deformation condition of a landslide region in real time; the ground laser scanning equipment is used for acquiring three-dimensional space data of a landslide area; the unmanned aerial vehicle carries a high-resolution camera and is used for collecting high-resolution image information of the landslide area; and the data processing center is used for receiving data collected by the ground-based interferometric synthetic aperture radar, the ground laser scanning and the unmanned aerial vehicle and carrying out data fusion and analysis and early warning information generation. According to the emergency monitoring method, data collected by the ground-based interferometric synthetic aperture radar device, the ground laser scanning device and the unmanned aerial vehicle are transmitted to the data processing center, and multi-source data are matched and integrated through a data fusion method; and analyzing the fused data, evaluating the landslide risk, and generating early warning information. The method is suitable for geographic information monitoring in emergency.

The invention relates to an interferometric synthetic aperture radar (InSAR)-based measurement method and system, and the method comprises the steps: determining the prior positions of a plurality of targets, and providing the prior positions to a ground processing unit; shooting an area where a plurality of targets are located by using an SAR satellite, and sending the obtained data to a ground processing unit; determining a phase difference between the targets for a single shot of the target; according to the determined phase difference and the SAR load wavelength, the slope distance between the targets is calculated through a ground processing unit; according to the slope distance and the satellite orbit information, the position relation between the targets is determined through a ground processing unit. The measuring system at least comprises a ground processing unit and an acquisition unit. According to the invention, shooting at the same track inclination angle and different track inclination angles can be realized, the limitation of heavy track shooting conditions in the prior art is broken through, and high-frequency and high-precision surface micro deformation measurement can be realized for different application scenes.

The invention provides a mining area mining monitoring method and system. The system comprises a synthetic aperture radar interference monitoring module, an optical remote sensing monitoring module and a microseismic monitoring module. The method comprises the following steps: a synthetic aperture radar interference monitoring module obtains synthetic aperture radar image data of a mining area, carries out surface subsidence monitoring on the mining area according to the synthetic aperture radar image data, and determines a target monitoring area of the mining area; and the optical remote sensing monitoring module obtains optical remote sensing image data of the target area, determines surface damage information according to the optical remote sensing image data of the target area and the synthetic aperture radar image data, and monitors mining behaviors of the mining area according to the surface damage information. The optical remote sensing image data comprises satellite image data and aerial image data; and the micro-seismic monitoring module obtains underground micro-seismic data of the targeted monitoring area, and carries out real-time monitoring and positioning on mining behaviors of the targeted monitoring area according to the micro-seismic data so as to realize all-directional monitoring of mining area mining.

The present invention discloses an InSAR-based measurement method and system for angle of critical deformation in a coal mining area, and relates to the technical field of geological disaster prevention and control of mountainous coal mines, comprising: acquiring a mining surface deformation field image using a small baseline subset interferometric synthetic aperture radar; and drawing profile lines along the strike direction and dip direction of the coal seam respectively in the mining surface deformation field image. The method provided by the present invention has the advantages of improving time resolution and reducing phase noise.

The application provides a UAV-InSAR-based magnetic suspension system multi-point collaborative reinforcement learning compensation control method, system, device and medium, the method comprises the following steps: using a UAV to carry a lightweight X-band interferometric synthetic aperture radar system to carry out high-precision surveying and mapping on a magnetic suspension line, and constructing an orbit irregularity power spectrum model; a hierarchical control system is constructed, the amplitude saturation controller in the lower layer generates a basic electromagnetic force control law according to the state quantity of the current suspension point; the upper layer compensation controller based on reinforcement learning is an agent, interacts with the environment composed of the suspension frame, the track and the amplitude saturation controller, trains by using the standardized irregularity function obtained from the track irregularity power spectrum model, and outputs the compensation control quantity for each suspension point; the basic electromagnetic force control law and the compensation control quantity are superimposed to generate the total electromagnetic force control law of each suspension point, and the suspension system is actively compensated and globally stably suspended. The application significantly improves the comprehensive control performance of the suspension system.

The invention relates to an InSAR (Interferometric Synthetic Aperture Radar) submillimeter-level high-precision deformation quantity detection method based on initial phase error elimination, and belongs to the field of electronic signal processing. Firstly, millimeter wave radar equipment is used for monitoring and scanning a target area and realizing SAR imaging, and main and auxiliary images are divided according to a radar scanning period; a PS point is selected, and the deformation quantity of the PS point is used for representing the deformation state of the whole target area; then taking the distance from the PS point to the original point of the image as a reference, combining with the radar instantaneous coordinate corresponding to each pulse signal, extracting phase information of the corresponding PS point from all the pulse signals subjected to distance compression, and calculating a phase mean value to eliminate an initial phase error of a transmitted signal; and finally, for the same PS point, calculating a phase difference between two monitoring processing results to obtain a real deformation quantity. According to the method, the error generated by the random fluctuation of the initial phase of the transmitted signal is reduced, the traditional calculation method is simplified, and the calculation efficiency and the calculation precision are both improved.

The application discloses a non-line-of-sight building layout relocation method and device in array interferometric synthetic aperture radar three-dimensional imaging, a storage medium and a program product, and is applied to the technical field of array interferometric synthetic aperture radar three-dimensional imaging. The method is aimed at the problem that the prior art cannot realize non-line-of-sight building layout relocation when building layout parameters are unknown. The method comprises the following steps: firstly, an array interferometric synthetic aperture radar system is used to obtain an initial three-dimensional point cloud of an observation scene; then, an Alpha Shape algorithm is applied to surface reconstruction of the point cloud to construct a boundary model for reflection path analysis; then, whether a line connecting the point cloud and a radar antenna intersects with a surface model is analyzed to identify non-line-of-sight points and associated reflection points; finally, a density clustering and plane fitting algorithm is used to correct and relocate the non-line-of-sight building layout in combination with a mirror symmetry geometric relationship. The method does not need prior structural information, can effectively identify a multipath reflection path, restore the real spatial position of a building in a non-line-of-sight area, significantly improves three-dimensional imaging precision, and provides strong support for SAR applications such as city perception and structure reconstruction.

The invention relates to the technical field of geological disaster monitoring, and discloses a goaf collapse risk early warning method and system based on a multi-source InSAR. The method comprises the following steps: acquiring initial deformation time sequence data of a goaf through a continuous scatterer interferometric synthetic aperture radar and determining a reference point; phase calculation is carried out on the distributed scatterer interferometric synthetic aperture radar deformation data based on the reference points, fitting correction is carried out in combination with continuous scatterer points located in a goaf, and a fused earth surface deformation monitoring result is obtained; underground space structure information is obtained and rasterized, and rasterized underground structure information is formed; calculating a first risk value according to the fused earth surface deformation monitoring result and the rasterized underground structure information, calculating a second risk value according to future temperature and accumulated snow prediction, and calculating a third risk value according to future rainfall prediction; and determining goaf collapse risk early warning information according to the three types of risk values. According to the method, goaf collapse risk identification precision and early warning timeliness are improved.

This application discloses a method for assessing transmission line settlement risk, as well as a storage medium and computer equipment. The method includes: calculating pore water pressure by considering rainfall and dynamic processes, and then calculating the slope sliding velocity in the target area at different times. Based on a rainfall-driven creeping landslide collapse model, a dynamic equilibrium differential equation for the landslide is constructed. The equation and the slope sliding velocity are used to predict the time required for a potential landslide in the target area to progress from slow creep to collapse, thereby obtaining the slope collapse rate. Deformation data is obtained based on an interferometric synthetic aperture radar deformation method. The deformation data is used as one of the landslide driving factors and is input into three machine learning methods along with other driving factors to obtain landslide susceptibility, i.e., landslide probability. Combining landslide probability with slope collapse rate to assess landslide risk along transmission towers improves the interpretability and accuracy of landslide risk assessment, providing strong support for landslide disaster prevention and post-disaster repair of transmission lines.

The application discloses an InSAR deformation data processing method, and relates to interferometric synthetic aperture radar. Isoline data is extracted from DEM data of an InSAR monitoring area, and the InSAR monitoring area is divided into a plurality of sub-areas; water body data is obtained by extracting the number of water bodies in the monitoring area according to SAR data intensity maps or optical images; pre-processing is performed on InSAR deformation data, such as abnormal value removal and neighborhood analysis, to obtain pre-processed deformation data; according to the water body data, deformation values distributed in water areas in the InSAR data are removed to obtain new InSAR deformation data; data correction processing is performed on the InSAR deformation data and sub-area distribution data to obtain corrected InSAR deformation data; and the corrected InSAR deformation data is rendered to generate a deformation map of the monitoring area. The method is simple, data is easy to obtain, overall error is reduced, and the precision of InSAR deformation data in a small range and local area is improved.

The present invention relates to the technical field of interferometric synthetic aperture radar (ISA), and discloses a micro-scale deformation monitoring system based on ISA, comprising a data acquisition module, a deformation analysis module, and a monitoring and early warning module. The data acquisition module comprises a data acquisition unit, a data preprocessing unit, and a data error early warning unit, and the deformation analysis module comprises a first analysis unit, a second analysis unit, and a comprehensive analysis unit. The system uses ISA to collect deformation variables at several different points of a target location, simultaneously obtains physical environmental factors during the data collection process, and analyzes and obtains an error influence coefficient. Simultaneously, the system calculates an overall deformation coefficient based on the deformation variables at different points of the target location, calculates an overall deformation trend based on the deformation variables at different points of the target location, and comprehensively calculates a comprehensive risk coefficient to determine the micro-scale deformation risk of the target location, reduce interference with the data caused by environmental and physical factors, and ensure the accuracy of the analysis.

This invention relates to a method for registering three-dimensional point clouds using array interferometric synthetic aperture radar (InSAR). The method utilizes an airborne platform equipped with an array InSAR antenna to acquire array SAR images, performs image registration, and calculates multi-band three-dimensional point clouds pixel by pixel. It calculates the nearest distance to each point in the point cloud, averages all distance values ​​to obtain the point cloud resolution, extracts strong target points, and constructs intensity feature descriptors to register the array InSAR three-dimensional point clouds. This method is suitable for solving the problem of difficult registration of array InSAR three-dimensional point clouds, improving the relative geometric accuracy between array InSAR point clouds obtained from different flight zones, and is beneficial for the mapping applications of array InSAR technology.

This invention discloses an atmospheric delay correction method and system for interferometric synthetic aperture radar (InSAR), belonging to the field of radar signal correction technology. It includes: performing time-series analysis on a preprocessed SAR image set to obtain multiple interferograms; performing phase unwrapping to obtain multiple unwrapped phases; decomposing the observation matrix composed of the multiple unwrapped phases to obtain multiple independent components; removing independent components related to vertical layer delay from the observation matrix based on the correlation coefficient and periodicity coefficient between each independent component and terrain elevation to obtain a residual phase matrix; and performing turbulent delay correction on the residual phase matrix to obtain a corrected interferogram phase set deformation time series. This invention effectively suppresses the interference of atmospheric delay errors on deformation signals, preserves the true deformation information to the greatest extent, and significantly improves the accuracy of InSAR deformation monitoring.