Urban Surface Deformation Mnitoring And Cause Analysis Based On Optical And SAR Images Fusion

The occurrence of urban land subsidence disaster affects the development and construction of the city.Among them,rainfall,underground shield and ground construction load may cause rapid changes in land subsidence,which will lead to the settlement disaster.Therefore,if the settlement can be monitored in time and the cause of formation can be analyzed,it can not only provide better decision-making for urban construction,but also greatly avoid the sudden accidents caused by settlement disaster,avoid casualties and economic losses.The traditional method of monitoring and cause analysis of land subsidence is mainly through a single data source for experimental research,such as the InSAR technology developed in modern times,which has the characteristics of all-weather,high precision and not easily affected by the weather in deformation monitoring,and gradually becomes a hot spot in deformation research field.The high-precision deformation results generated by this technology make it possible to study the subtle changes of settlement and the refinement of influence factors.After years of technological development and equipment update,the remote sensing data is becoming more and more diversified,and more and more remote sensing data can be selected.The existence of multi-source data makes it easier for researchers to obtain different remote sensing images in similar time Data.Based on the deformation results obtained by sbas-insar technology,this paper uses hyperspectral and SAR image data for image fusion and deformation factor extraction,not only for surface deformation monitoring,but also for analysis and Research on the causes of surface deformation.The main research contents and achievements of this paper are summarized as follows:(1)Using sentinel-1 SAR data of September 24,2019 and sentinel-2 MSI data of September 29,2019,traditional HSV,Brovey,GS and other methods are used for multisource image fusion of the two sources of data,and the differences and characteristics of different fusion methods are analyzed.(2)Based on the analysis of the fused image,the geographical characteristics and deformation of the research area,and the demand of the following analysis of rainfall,ground load and subway operation,the vegetation coverage area and the area of interest of the ground buildings under construction are selected for the feature recognition,and the constrained energy minimization(CEM)method and adaptive Consistency Estimation(ACE)method are respectively used Methods and hybrid tuned matched filtering(mtmf)are compared and analyzed,and the best method of classification is determined.(3)SBAS-InSAR method is used to analyze the ground deformation.Combined with the ground feature classification of the fusion image,the deformation factors are extracted and studied.Then the influence factors are calculated and analyzed by the multi factor superposition calculation weight method.The obtained deformation results are compared with the factors,and the influence degree of each factor on the deformation is analyzed.Taking rainfall as an example,the reliability of the weight is verified,which provides basic information for the analysis of settlement cause.