Research On Fast Acquisition And Effective Recovery Method Of Glacier Velocity Field From SAR

Glacier movement is one of the important components of global climate change.It can provide intuitive research data for climate change and sea level change by monitoring glacier movement.The glacier velocity field,as the most important monitoring indicator in glacier movement,is the basis for studying glacier material distribution and glacier movement.Synthetic Aperture Radar Interferometry enables full-day,all-weather,and large-area monitoring of glacier motion,and high-precision glacier velocity fields can be obtained with offset tracking technology.However,due to the excessive monitoring range of the glacier area,the calculation of the offset tracking technology is too complicated.Therefore,the high-performance implementation of the offset tracking technology must be implemented to quickly obtain the glacier velocity field.At the same time,due to the decoherence of SAR images obtained from glacial areas,it is impossible to restore a comprehensive and continuous glacier velocity field in some areas.Therefore,it is necessary to explore an effective recovery method for the missing portion of the flow rate to obtain a complete glacier velocity field.In view of the above problems,this thesis studies the rapid acquisition and effective recovery of glacier velocity field.The main work and contributions of this thesis mainly include the following three points:1)This thesis completed the high-performance implementation of the offset tracking technology based on CUDA(Compute Unified Device Architecture).First of all,the basic principles of synthetic aperture radar and its interferometry technology are expounded,and the limitation of obtaining the velocity field of glacier by synthetic aperture radar interferometry is illustrated,and the offset tracking technology is introduced.Then,the offset tracking technology based on cross-correlation method is analyzed in detail,and a high-performance implementation scheme is proposed for the problem of excessive computational complexity in the scene of glacier velocity field acquisition.Finally,this thesis uses the memory management strategy of the CPU(Central Processing Unit)and the high-performance computing of the GPU(Graphics Processing Unit),the offset tracking technology is efficiently implemented by CUDA,and the rapid acquisition of the glacier velocity field is completed.It also improves the applicability of offset tracking technology in glacier velocity field monitoring.2)This thesis established a model of glacier velocity field recovery based on Kriging method.Above all,the principle of ordinary Kriging interpolation and cooperative Kriging interpolation are introduced.Then,this thesis selected the appropriate regionalization variables,established the corresponding semi-variance function model,and completed the restoration of the glacier velocity field by two Kriging interpolation methods.Finally,qualitative and quantitative comparisons were made between the recovery results of the two methods,and the effect of the Kriging method on recovering the velocity field of the glacier was analyzed.3)This thesis established a model of glacier velocity field recovery based on deep neural network.Firstly,according to the characteristics of deep neural network,a deep neural network is used to recover the glacier velocity field model,the corresponding hyperparameters and training methods were determined,and the part of the glacier velocity field with serious coherence was restored.Then,this thesis used the gradient information of the glacial area as an assist,the comprehensiveness and continuity of the recovery results are improved.Finally,by comparing with the results of two Kriging interpolation and pre-optimal depth neural networks,the role of deep neural network in the recovery of glacier velocity field is verified.