Ground Deformation Retrieval Using Quasi Coherent Targets DInSAR, With Application to Suburban Area of Tianjin, China

This paper presents a multiimage DInSAR approach to obtain ground movement using the quasi coherent targets (Q-CTs), such as the bare land and light cultivated areas that distribute widely on the suburban ground surface. The method adopts a new processing strategy for targets selection based on the classification information and coherence properties of the ground objects. Furthermore, the linked coherence model is proposed by defining the parameter of linked coherence on the neighboring Q-CTs network to retrieve the deformation and topographic estimations through multiimage DInSAR analysis. The suburban areas of Tianjin China, with fast township industry development, have been investigated in this study and 21 scenes of ALOS PALSAR dataset, covering the time period August 2007 to November 2010, have been gathered for the algorithm experiment. The experiment results show that there are obvious subsidence patterns detected in this region with subsidence velocity larger than 2 cm/year during the observation period, which have been well validated by both field surveys and the leveling measurements. This study demonstrates the potential of the Q-CTs based DInSAR analysis in the subsidence monitoring of large scale nonurban areas.

[1]  Gianfranco Fornaro,et al.  A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms , 2002, IEEE Trans. Geosci. Remote. Sens..

[2]  Mingsheng Liao,et al.  Three Gorges Dam stability monitoring with time-series InSAR image analysis , 2011 .

[3]  Claudio Prati,et al.  A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Fabio Rocca,et al.  Modeling Interferogram Stacks , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Daniele Perissin,et al.  Recent advances on surface ground deformation measurement by means of repeated space-borne SAR observations , 2010 .

[6]  Teng Wang,et al.  Monitoring terrain motion in China by means of spaceborne SAR images , 2009, 2009 Joint Urban Remote Sensing Event.

[7]  Stefano Tebaldini,et al.  On the Exploitation of Target Statistics for SAR Interferometry Applications , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Yunwei Tang,et al.  Deformation retrieval in large areas based on multibaseline DInSAR algorithm: a case study in Cangzhou, northern China , 2008 .

[9]  Yngvar Larsen,et al.  InSAR Deformation Time Series Using an $L_{1}$ -Norm Small-Baseline Approach , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Fabio Rocca,et al.  Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry , 2000, IEEE Trans. Geosci. Remote. Sens..

[11]  H. Zebker,et al.  Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcán Alcedo, Galápagos , 2007 .

[12]  Francesco De Zan,et al.  Coherent processing of long series of SAR images , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[13]  Jordi J. Mallorquí,et al.  Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..