Research on quality improvement method of deformation monitoring data based on InSAR

Abstract In recent years, geological disasters caused by surface deformation frequently occur, which seriously threatens the safety of people's lives and property. Therefore, it is of great significance to strengthen the monitoring of surface deformation. With the continuous advancement of science and technology, traditional monitoring technology is difficult to meet the development requirements of modern society. As a new type of space-to-earth observation technology, INSAR technology has the advantages of high precision and real-time dynamic monitoring, and has been obtained in surface deformation monitoring widely used. This paper briefly analyzes the basic working principle of INSAR technology and its specific application in surface deformation monitoring. The algorithm parallelism of the ground-based SAR deformation monitoring process is analyzed, and the CPU + GPU heterogeneous platform is used to accelerate the implementation to improve the timeliness of deformation monitoring. BP imaging algorithm, interferogram generation, interferogram filtering and phase unwrapping algorithm are designed in parallel, and appropriate parallel granularity planning for multiple loops, adaptive division of optimal thread block size and use of shared memory to reduce duplicate data are adopted. Optimization strategies such as read time enable GPU acceleration processing. Compared with the implementation of CPU platform and CPU + GPU heterogeneous platform, the acceleration effect from tens to hundreds of times is accelerated, and the feasibility of GPU to improve the timeliness of deformation monitoring is verified.

[1]  Yan Jiang,et al.  City subsidence observed with persistent scatterer InSAR , 2010 .

[2]  Saadi Boudjit,et al.  Sparse optimization of non separable vector lifting scheme for stereo image coding , 2018, J. Vis. Commun. Image Represent..

[3]  H. Zebker,et al.  A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers , 2004 .

[4]  F. Rocca,et al.  InSAR Principles-Guidelines for SAR Interferometry Processing and Interpretation , 2007 .

[5]  J. Nocquet,et al.  Slip distribution of the February 27, 2010 Mw = 8.8 Maule Earthquake, central Chile, from static and high‐rate GPS, InSAR, and broadband teleseismic data , 2010 .

[6]  H. Zebker,et al.  Sensing the ups and downs of Las Vegas: InSAR reveals structural control of land subsidence and aquifer-system deformation , 1999 .

[7]  Kurt L. Feigl,et al.  Crustal deformation and fault slip during the seismic cycle in the North Chile subduction zone, from GPS and InSAR observations , 2004 .

[8]  Moacir Antonelli Ponti,et al.  Generalization of feature embeddings transferred from different video anomaly detection domains , 2019, J. Vis. Commun. Image Represent..

[9]  H. Zebker,et al.  Measuring two‐dimensional movements using a single InSAR pair , 2006 .

[10]  Vito Pascazio,et al.  Multifrequency InSAR height reconstruction through maximum likelihood estimation of local planes parameters , 2002, IEEE Trans. Image Process..

[11]  Peter J. Clarke,et al.  Atmospheric models, GPS and InSAR measurements of the tropospheric water vapour field over Mount Etna , 2002 .

[12]  T. Wright,et al.  InSAR Observations of Low Slip Rates on the Major Faults of Western Tibet , 2004, Science.

[13]  Lambert Schomaker,et al.  Multi-script text versus non-text classification of regions in scene images , 2019, J. Vis. Commun. Image Represent..

[14]  Liyanage C. De Silva,et al.  Visual data of facial expressions for automatic pain detection , 2019, J. Vis. Commun. Image Represent..

[15]  Vito Pascazio,et al.  Maximum a posteriori estimation of height profiles in InSAR imaging , 2004, IEEE Geoscience and Remote Sensing Letters.

[16]  Fabio Rocca,et al.  Multibaseline InSAR DEM reconstruction: the wavelet approach , 1999, IEEE Trans. Geosci. Remote. Sens..

[17]  T. Wright,et al.  Toward mapping surface deformation in three dimensions using InSAR , 2004 .

[18]  Irena Hajnsek,et al.  Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Florence Tupin,et al.  NL-InSAR: Nonlocal Interferogram Estimation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[20]  L. Rivera,et al.  Coseismic Deformation from the 1999 Mw 7.1 Hector Mine, California, Earthquake as Inferred from InSAR and GPS Observations , 2002 .

[21]  Wei Xu,et al.  A region-growing algorithm for InSAR phase unwrapping , 1999, IEEE Trans. Geosci. Remote. Sens..

[22]  Uwe Soergel,et al.  Potential and limits of InSAR data for building reconstruction in built-up areas , 2003 .

[23]  Tim J. Wright,et al.  Post-seismic motion following the 1997 Manyi (Tibet) earthquake: InSAR observations and modelling , 2007 .

[24]  Zheng Bao,et al.  Image autocoregistration and InSAR interferogram estimation using joint subspace projection , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Alessandro Parizzi,et al.  Adaptive InSAR Stack Multilooking Exploiting Amplitude Statistics: A Comparison Between Different Techniques and Practical Results , 2011, IEEE Geoscience and Remote Sensing Letters.

[26]  Mohammad Reza Taban,et al.  Nonparametric blind SAR image super resolution based on combination of the compressive sensing and sparse priors , 2018, J. Vis. Commun. Image Represent..