Abstract: 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

Abstract: A surface deformation monitoring method and systems based on time-series InSAR (TS-InSAR) are provided. The method includes acquiring Synthetic Aperture Radar (SAR) data; performing differential interferometry; performing a robust two-tier multi-temporal InSAR method for detection of Persistent Scatterer (PS) and Distributed Scatterer (DS) candidates to acquire time series data for surface deformation monitoring; performing a variational mode decomposition (VMD) method to decompose TS-InSAR data into a plurality of components; reconstructing time series data; performing a gated recurrent unit (GRU) method; extracting trend of surface deformation; and performing continuous large-scale deformation monitoring. The InSAR-based deformation monitoring method integrates VMD and GRU, offering significant improvements in robustness and accuracy over the existing methods.

Abstract: A Multi-temporal Interferometric Synthetic Aperture Radar method for jointly detecting persistent scatterers (PSs) and distributed scatterers (DSs) is provided. The method includes in a first-tier network, selecting a plurality of PSs if a value of temporal coherence of the PS is larger than a first predetermined threshold; performing beamforming and M-estimator to enhance robustness of parameter estimation; in a second-tier network, identifying new PSs from the plurality of PSs by performing same prior steps; continuously updating the network based on the newly selected PSs until the value of the temporal coherence is smaller than the first predetermined threshold; performing coherence-weight phase-linking to reconstruct optimal phases for detection of a distributed scatterer (DS); and identifying that a pixel is a DS if the temporal coherence is greater than a second predetermined threshold based on the reconstructed optimal phases.

Abstract: 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

Abstract: A method for InSAR assessment and prediction of risk level of urban buildings is provided, and includes the following steps: obtaining multi-channel SAR images of the urban buildings, extracting monitoring points in the multi-channel SAR images, and obtaining InSAR monitoring results of the urban buildings based on areas of the monitoring points, wherein the monitoring points comprise permanent scatterers and distributed scatterers; obtaining building outlines, obtaining deformation parameters based on the building outlines and the InSAR monitoring results, and assessing a building risk level based on the deformation parameters, wherein the deformation parameters comprise deformation velocity parameters and angle distortion parameters; constructing a stress-pore water pressure model, and carrying out a building risk prediction on the numerical simulation results based on set building risk level.

Abstract: A method for InSAR assessment and prediction of risk level of urban buildings is provided, and includes the following steps: obtaining multi-channel SAR images of the urban buildings, extracting monitoring points in the multi-channel SAR images, and obtaining InSAR monitoring results of the urban buildings based on areas of the monitoring points, wherein the monitoring points comprise permanent scatterers and distributed scatterers; obtaining building outlines, obtaining deformation parameters based on the building outlines and the InSAR monitoring results, and assessing a building risk level based on the deformation parameters, wherein the deformation parameters comprise deformation velocity parameters and angle distortion parameters; constructing a stress-pore water pressure model, and carrying out a building risk prediction on the numerical simulation results based on set building risk level.

Abstract: This invention relates to a pipe-like appendage for a sharp-edged pontoon of an offshore vessel, a pontoon structure comprising the pipe-like appendage and a method of converting a sharp-edged pontoon into a pontoon comprising at least one rounded-edge.

Abstract: Disclosed is a method for 3D building extraction, comprising: extracting building footprints from one or more stereo images for a building; determining, from rational polynomial coefficient of the stereo images, first height estimation of the extracted building footprints; obtaining, from multi-temporal SAR images for the building, scatters with stable attributes; determining second height estimation for the determined scatters; and combining the first height estimation and the second height estimation to generate a fused height for each of the extracted building footprints.

Abstract: This invention relates to a pipe-like appendage for a sharp-edged pontoon of an offshore vessel, a pontoon structure comprising the pipe-like appendage and a method of converting a sharp-edged pontoon into a pontoon comprising at least one rounded-edge.

Abstract: Disclosed is a method for 3D building extraction, comprising: extracting building footprints from one or more stereo images for a building; determining, from rational polynomial coefficient of the stereo images, first height estimation of the extracted building footprints; obtaining, from multi-temporal SAR images for the building, scatters with stable attributes; determining second height estimation for the determined scatters; and combining the first height estimation and the second height estimation to generate a fused height for each of the extracted building footprints.