Complex scene deformation monitoring and classifying method based on InSAR and deep learning self-attention model

A complex scene deformation monitoring and classifying method based on interferometric synthetic aperture radar (InSAR) and a deep learning self-attention model, comprising:

• S1:
• performing a co-registration between a first SAR image from a first satellite constellation and a second SAR image from a second satellite constellation by using an improved InSAR extraction technology of PS points and DS points and relying on an assistance of a shuttle radar topography mission digital elevation model (SRTM DEM) image, and generating an interferogram;
• S2:
• constructing a Delaunay triangulation network and an annular zone on a basis of the generated interferogram, obtaining time series deformation data of all PS points and DS points through adaptive arc densification and omni-directional point expansion, and extracting settlement points on a bridge;
• S3:
• synthesizing an InSAR time series sample through the time series deformation data, decomposing a time series by using an SAR-self-attention model, so as to decompose InSAR time series data into a trend component and a seasonal component, and accurately interpreting and analyzing deformation of a sea-crossing bridge;
• and
• S4:
• analyzing the generated deformation time series, describing time dynamics and a seasonal pattern after the time series is decomposed by comparing a curve fitting method with a seasonal and trend decomposition method using locally weighted scatterplot smoothing (LOESS).