20 results about "Permanent scatterer" patented technology

The invention discloses an adjacent foundation pit deformation prediction method for a dense building group and a support deformation prediction method, and the method comprises the steps: obtaining SAR satellite images of a to-be-processed region in a period of time sequence, and generating an image stack after registration; screening points of which the amplitude deviation indexes are lower than a threshold value as PS points; calculating the relative displacement sequence of each PS point, inverting the displacement variation and accumulating to obtain the accumulated settlement; before construction, a digital surface model (DSM) is generated through aerial survey of an unmanned aerial vehicle, feature points with stable displacement are recognized by combining SAR images, and the feature points are anchored to an absolute coordinate system of the unmanned aerial vehicle for correction. Therefore, permanent scatterer (PS) identification and time sequence analysis are carried out by utilizing SAR images of a long time sequence, absolute geographic reference is provided by a high-precision digital surface model (DSM) generated by aerial survey of an unmanned aerial vehicle, and reference correction is carried out on a relative displacement field acquired by the InSAR.

The embodiment of the invention discloses a hydroelectric slope deformation monitoring method, a hydroelectric slope deformation monitoring system and electronic equipment. The deformation monitoring method comprises the following steps: acquiring an optical image stream, a radar image sequence and a meteorological data set; performing feature extraction on the optical image, and obtaining a target displacement sequence; performing lens inclination angle distortion correction based on the target displacement sequence to obtain optical correction data; the method comprises the following steps: acquiring a permanent scatterer deformation sequence by adopting an SBAS-InSAR technology based on a radar image sequence, generating a global deformation graph sequence by adopting a spatial interpolation algorithm, carrying out space-time alignment on a target displacement correction sequence and a meteorological data set with the global deformation graph sequence, and carrying out atmospheric delay correction based on an obtained time distribution matrix. Calibrating the radar data based on the optical data to obtain corrected radar deformation data; and hydroelectric slope deformation monitoring information is output by fusing the optical displacement data and the radar deformation data. According to the invention, the precision, timeliness and environment adaptability of deformation monitoring are enhanced.

The application relates to a kind of global settlement monitoring methods of electric power substation, electronic equipment and medium, the method comprises: obtaining the time series settlement data of electric power substation based on PS-InSAR technology, the grid division of electric power substation area is carried out based on time series settlement data, and permanent scatterer at the intersection of grid is selected as key settlement monitoring point;Beidou settlement monitoring machine base station is set in the periphery of electric power substation, and a Beidou settlement monitoring machine station is set in the middle area of electric power substation to obtain the settlement data of the position;The settlement data of Beidou settlement monitoring machine is input into the dynamic fitting function constructed, and the settlement value of key settlement monitoring point in electric power substation is calculated in real time, wherein the dynamic fitting function represents the dynamic relationship between the settlement data of Beidou settlement monitoring machine and the settlement data of key settlement monitoring point.Compared with prior art, the application has the advantages of realizing global, real-time settlement monitoring of electric power substation at low cost and the like.

Embodiments of the present application disclose a kind of power transmission line three-dimensional deformation monitoring method and system, equipment and storage medium, method includes: using the preliminary elevation estimate of permanent scatterer point on power transmission line and in the plane coordinate of multi-temporal synthetic aperture radar image, construct the three-dimensional space network of permanent scatterer point;For each arc segment of three-dimensional space network, establish fine phase model, and calculate the coherence coefficient of the observation value of each arc segment differential interferometric phase and the fitting value of differential interferometric phase under different model parameter combinations, to generate the coherence coefficient solution space of each arc segment;Using the preset adaptive threshold detection method, according to the local background statistical characteristics of coherence coefficient in coherence coefficient solution space, dynamic decision threshold is determined to identify high-quality deformation arc segment;According to the model parameters corresponding to high-quality deformation arc segment, the three-dimensional deformation of power transmission line is determined.A kind of high-precision, strong robustness improved time series interferometric synthetic aperture radar deformation monitoring method is realized.

The invention belongs to the technical field of synthetic aperture radar interferometry, and particularly relates to a PS-InSAR multi-baseline time sequence phase unwrapping method based on uniform variable speed deformation assumption, which comprises the following steps of: preprocessing multi-temporal SAR data to obtain a time sequence difference interferogram, selecting a permanent scatterer PS point on the time sequence interferogram, and performing uniform variable speed deformation assumption on the selected permanent scatterer PS point; a second-order phase gradient constraint model is constructed on the time dimension under the assumption of uniform variable speed deformation, the integer fuzzy number gradient of the second-order phase gradient constraint model is solved by adopting a bidirectional time marching algorithm, and a global weighted L1 norm optimization problem about an absolute phase vector is constructed and solved based on the integer fuzzy number gradient. According to the method, the acceleration component in the earth surface deformation process is effectively described by establishing the second-order phase gradient constraint model, and the adaptability to complex time sequence deformation is improved.

The invention relates to the technical field of road safety, and discloses a road damage safety dynamic evaluation method and system. The method comprises the following steps: acquiring road vector data and attribute information from a preset data source, and generating an initial road influence buffer area; calculating the gradient and the slope direction by utilizing the elevation data of the initial buffer area, and expanding to obtain a to-be-detected buffer area; coherent point deformation information is obtained based on permanent scatterer synthetic aperture radar interferometry; time sequence deformation information obtained through distributed scatterer synthetic aperture radar interference measurement is combined, and deformation fusion data are formed through fusion; building information is extracted through high-resolution optical satellite images and tomographic synthetic aperture radar data, and resident activity indexes are calculated; correlation index data including gradient, time sequence radar image intensity and the like are calculated; and calculating a road damage threat index in combination with the deformation fusion data, the resident activity index and the associated index. According to the method, road damage safety dynamic evaluation is realized, and the accuracy and the regional applicability are improved.

The invention relates to the technical field of deformation time sequence analysis, in particular to a hydro-junction InSAR deformation time sequence analysis method and system, and the method comprises the following steps: screening hydro-junction radar images to construct a main and auxiliary data set, generating a differential interferogram, extracting a permanent scatterer, inverting a deformation speed, mapping the deformation speed to a geographic coordinate system, and outputting an InSAR deformation time sequence analysis result. According to the invention, through constructing a radar image data set covering a key structure area, stripping terrain and atmospheric interference factors, extracting long-term stable scatterer points and calculating the deformation speed and elevation error of the scatterer points, continuous time sequence analysis of structure deformation is realized; the result precision and the anti-interference capability are improved through multi-source data fusion and phase expression optimization, high-quality deformation information inversion is completed in combination with orbit parameters and atmospheric correction data, the recognition capability of the stability change trend of key structures is enhanced, and efficient monitoring of wide-area coverage, high timeliness and low dependence on hydro-junction facilities is achieved.

The invention provides a GB-SAR atmospheric delay phase correction method based on clustering analysis, and the method comprises the steps: collecting a plurality of single-view complex images of a plurality of time phases through GB-SAR equipment, generating a short-baseline interferogram pair, and carrying out the screening of a permanent scatterer point; based on preset large-scale atmospheric phase correction, performing model fitting on the permanent scatterer points to obtain a large-scale estimated value; based on preset small-scale atmospheric phase correction, clustering the permanent scatterer points through spatial dimension clustering and time dimension clustering to obtain a space-time sample cluster, and calculating or complementing the space-time sample cluster to obtain a small-scale estimated value; and adding the large-scale estimation value and the small-scale estimation value to obtain a total atmospheric phase estimation value. According to the method, the large-scale fitting advantage of the PSM method and the phase stacking thought of the CSS method are combined, homogeneous samples are obtained through space and time two-dimensional clustering analysis, and accurate removal of small-scale turbulent atmosphere is achieved.

The power transmission tower vibration deformation measuring and early warning method based on the ground-based synthetic aperture radar comprises the steps that scanning imaging is conducted on a power transmission tower through the ground-based synthetic aperture radar, and a plurality of radar image sequences which cover a target tower body and are arranged according to the time sequence are obtained; processing the plurality of radar image sequences by adopting a permanent scatterer interference measurement method, extracting a plurality of permanent scatterer points attached to the key points of the power transmission tower, and obtaining a displacement time sequence of each permanent scatterer point along the sight line direction of the radar; decomposing the displacement time sequence along the radar sight line direction into deformation component time sequences in three orthogonal directions under a power transmission tower structure coordinate system; joint time-frequency analysis and modal parameter identification are carried out on the deformation component time sequence in each direction, and modal parameters of the power transmission tower are extracted; establishing a multi-level early warning mechanism, calculating the change indexes of the current modal parameter and the deformation component relative to the reference value, and automatically triggering different levels of power transmission tower structure state early warning.

The invention discloses a self-adaptive high-precision terrain phase error compensation method for a GNSSINBSAR (Global Navigation Satellite System-Single Binary Synthetic Aperture Radar) system based on multi-satellite collaborative observation. The method comprises the following steps: firstly, providing a multi-angle SAR image association algorithm to realize association of the same terrain point in different satellite images; secondly, designing an improved robust estimator and improving a loss function and a weighting function of the estimator in combination with a non-robust data identification and elimination mechanism so as to effectively suppress the influence of an outlier observation value and realize robust estimation of the terrain error of the permanent scatterer; besides, on the basis of a position precision factor (PDOP), the influence of a multi-satellite observation configuration on deformation inversion precision can be quantified, a weighted spatial filter fusing PDOP and PS point spatial distances at the same time is provided, the advantages of multi-satellite collaborative observation are fully utilized, a full-scene high-precision terrain error estimation result is obtained, terrain phase error compensation is achieved, and the precision of deformation inversion is improved. And therefore, the deformation inversion precision and effective early warning of disasters can be improved.

This application relates to the technical field of geographic surveying, and in particular to a ground subsidence monitoring method and system based on air-space-ground collaboration. The method includes acquiring ground-based meteorological data, vegetation moisture content, and transit time windows; based on the transit time window, when the ground-based meteorological data and vegetation moisture content reach corresponding preset thresholds, issuing a flight escort command to an airborne vehicle; acquiring the echo signal from the airborne vehicle and generating a spatial blocking mask; extracting candidate permanent scatterers from regions where the mask is assigned a value of one in the interferometric phase diagram, and performing occlusion verification to screen out confirmed permanent scatterers; assigning weights to the spatial solution nodes within the interferometric phase diagram; constructing a topology network based on the weighted spatial solution nodes, ensuring that the unwrapped integral paths in the topology network avoid zero-weight nodes, performing obstacle avoidance phase unwrapping, and outputting ground subsidence monitoring results. This application can overcome the incoherence interference of canopy water cover, thereby extracting the true characteristics of the underlying surface subsidence.

The application discloses a bridge deformation monitoring method based on permanent scatterer interference, first uses a traditional PS-InSAR method and only models height phase to solve PS point height information, then uses an improved free combination network combined with threshold screening to obtain more accurate height information, long-term settlement deformation information and periodic thermal expansion deformation information by using the height information obtained in the first step; the PS-InSAR method of the application through step-by-step iteration and combined with a high redundancy network can monitor the deformation of a truss bridge, can obtain more accurate and robust height and deformation parameter estimation results, and therefore the deformation monitoring capability of the application is superior to that of the traditional PS-InSAR method.

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 geological catastrophe monitoring method for a diversion canal of a non-pressure diversion type hydropower station, and relates to the technical field of geological catastrophe monitoring. Determining a region of interest in the range of the target diversion canal according to the change of the slope ratio of the terrain along the way of the target diversion canal; expanding the direction of the window in the region of interest along the trend of the diversion canal, determining homogeneous pixels and performing phase optimization, and obtaining candidate distributed target pixels from the homogeneous pixels according to a phase consistency discrimination method; fusing the permanent scatterer candidate points in the time sequence synthetic aperture radar image with the candidate distributed target pixels, performing phase compensation according to the self-statistical point locations, and calculating the deformation value of each position in the range of the target diversion canal to determine a suspected geological catastrophe region; and determining the geological catastrophe area of the target diversion canal according to the high-resolution optical and thermal infrared images of the suspected geological catastrophe area and the deformation value. The method improves the recognition accuracy of the geological catastrophe along the diversion canal of the non-pressure diversion type hydropower station.

The application relates to the technical field of deformation monitoring, in particular to a subway line deformation risk monitoring method and system based on InSAR technology, which comprises the following steps: collecting surface height data of each permanent scatterer on a to-be-monitored subway line in real time, so as to obtain the settlement amount of each permanent scatterer at each time; obtaining the settlement synchronization value between any two permanent scatterers at each time, and obtaining the settlement coefficient of each permanent scatterer; screening each settlement factor point, obtaining the comparison factor between each permanent scatterer and each settlement factor point, and correcting the settlement coefficient of each settlement factor point; obtaining the settlement deviation weight and the settlement deviation coefficient of each permanent scatterer, and obtaining the ground settlement amount of each permanent scatterer; and predicting the ground settlement amount of each permanent scatterer at the next time, so as to monitor the deformation risk of the subway line. The application aims to improve the accuracy of subway line deformation risk assessment.

The present application relates to the technical field of deformation time series analysis, in particular to a water conservancy hub InSAR deformation time series analysis method and system, comprising the following steps: screening radar image of water conservancy hub to construct main and auxiliary data sets, generating differential interferogram, extracting permanent scatterers, inverting deformation velocity and mapping to geographic coordinate system, and outputting InSAR deformation time series analysis result. In the present application, by constructing radar image data set covering key structure area, stripping off terrain and atmospheric interference factors, extracting long-term stable scatterer points and calculating their deformation velocity and elevation error, continuous time series analysis of structure deformation is realized, through multi-source data fusion and phase expression optimization, result precision and anti-interference ability are improved, combined with orbit parameter and atmospheric correction data, high-quality deformation information inversion is completed, identification ability of key structure stability change trend is enhanced, and efficient monitoring of water conservancy hub facilities with wide area coverage, high timeliness and low dependence is realized.

The invention provides a scattering point offset correction-based InSAR deformation monitoring method for a power transmission tower in a flood inundated area. The method comprises the following steps: firstly, processing multi-temporal SAR image data by adopting a time sequence interference measurement method fusing a permanent scatterer and a short baseline set, and extracting time sequence deformation information of a high-coherence point target in a submerged area through adaptive spatial filtering and multiple phase iterations; and secondly, constructing an iron tower scattering point offset correction model based on a radar imaging geometric principle, and accurately identifying a high-coherence point range corresponding to the iron tower by calculating a theoretical offset direction and distance. Then, a space matching method of a geographic information system is combined, a buffer area is established with an iron tower base as the center, optimal point targets with high coherence and consistent deformation characteristics are screened out, a time sequence curve of the optimal point targets is extracted, and the actual deformation state of the iron tower is accurately reflected; according to the method, the problem of association errors caused by rare effective observation points and point location deviation in a submerged environment is effectively solved, and high-precision, non-contact and large-range monitoring of the deformation of the iron tower is realized.

The invention relates to the technical field of deformation monitoring, in particular to a subway line deformation risk monitoring method and system based on the InSAR technology, and the method comprises the steps: collecting the surface height data of each permanent scatterer on a subway line to be monitored in real time, and obtaining the settlement of each permanent scatterer at each moment; at each moment, a settlement synchronization value between any two permanent scatterers is obtained, and a settlement coefficient of each permanent scatterer is obtained; screening each settlement factor point, obtaining a contrast factor between each permanent scatterer and each settlement factor point, and correcting a settlement coefficient of each settlement factor point; the settlement deviation weight and the settlement deviation coefficient of each permanent scatterer are obtained, and the ground settlement amount of each permanent scatterer is obtained; and predicting the ground subsidence of each permanent scatterer at the next moment so as to carry out deformation risk monitoring on the subway line. The invention aims to improve the accuracy of subway line deformation risk assessment.

This invention discloses a feature fusion-based permanent scatterer (PSS) identification technique based on deep learning. First, a residual attention U-shaped network is constructed to extract spatial features from SAR images, and a C-LSTM network is constructed to extract temporal features from differential interferometric phase. Next, a feature fusion module is constructed to couple spatial and temporal features, and a prediction module is constructed to output dual-channel PSS identification results. Finally, single-channel PSS identification results are obtained by filtering through channel maximum values, and then image patch stitching and geocoding techniques are used to obtain the spatial distribution of PSS within the study area. This invention adopts a data-driven approach, enabling rapid identification of PSS through network training. The PSS identification technique provided by this invention employs a weighted summation of focal loss and residual loss to address the imbalance between positive and negative easy / negative samples and to evaluate the effectiveness of the identification results in downstream tasks. By utilizing the constructed feature fusion network and the mixed loss for PSS identification, this invention can identify a large number of PSS while maintaining a small number of residual points, making it more likely that the identification results will yield accurate surface deformation results when applied to downstream tasks.

The application discloses a permanent scatterer point extraction method, device, equipment and medium, the method is through obtaining multiple SAR images of different time phases, and the SAR images of different time phases are multi-level registration; wherein, the precision registration algorithm in the multi-level registration includes a coherent coefficient fitting method, and the coherent coefficient fitting direction is the tangent vector direction of the linear region in the SAR image; the linear region where the registered main image is located is subjected to image segmentation, and a super image is obtained; finally, the selection of the PS candidate point in each super pixel is carried out until the selected PS candidate point meets the relevant index parameter threshold condition and the density of the selected PS candidate point meets the preset PS point density threshold value, and finally, the PS point is obtained, the method can effectively overcome the defects that the distribution of the PS points extracted in the linear region is uneven and the density is low in the traditional PS point extraction algorithm, and the ground surface deformation detection precision is significantly improved. Accordingly, the application also provides a permanent scatterer point extraction device, a terminal equipment and a medium.