NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

When the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technology is used for surface deformation monitoring, the accuracy strongly depends on the quality of phase optimization. Especially, in the low-coherence region, how to conveniently and effectively improve the quality of phase optimization has been a hot and difficult research topic in recent years. This article proposes a hybrid phase optimization method for the DS-InSAR technology, which chooses either nonlocal (NL) or minimum mean square error (MMSE) method for each pixel according to the distribution of statistically homogeneous pixel in two windows of different sizes. This hybrid method (NL-MMSE) is not limited by the number of interferogram and is not influenced by multimaster images. The NL-MMSE method was applied to the deformation monitoring in the Jinsha River basin, Tibet, China, using 28 Sentinel-1A SAR images acquired between February and December 2020. Compared with the NL and MMSE methods, the NL-MMSE method provides better phase quality of the interferogram stack and is able to extract more temporal coherence points for subsequent deformation inversion. The deformation monitoring results showed there are three obvious large-scale landslide deformation and dozens of small-scale deformation in the study area. It is demonstrated that the NL-MMSE method based on DS-InSAR technology can accurately monitor the detailed deformation characteristics of the low-coherence surface, and can be applied and promoted as an effective means of identifying and monitoring geological hazards.

[1]  Chaoying Zhao,et al.  Two-dimensional deformation monitoring of karst landslides in Zongling, China, with multi-platform distributed scatterer InSAR technique , 2022, Landslides.

[2]  Junhuan Peng,et al.  Monitoring and Stability Analysis of the Deformation in the Woda Landslide Area in Tibet, China by the DS-InSAR Method , 2022, Remote. Sens..

[3]  Honglei Yang,et al.  Surface subsidence monitoring with an improved distributed scatterer interferometric SAR time series method in a filling mining area , 2021, Geocarto International.

[4]  Huifu Zhuang,et al.  An adaptive patch-based goldstein filter for interferometric phase denoising , 2021, International Journal of Remote Sensing.

[5]  Ling Chang,et al.  An improved distributed scatterers extraction algorithm for monitoring tattered ground surface subsidence with DSInSAR: A case study of loess landform in Tongren county , 2021, International Journal of Applied Earth Observation and Geoinformation.

[6]  Qianfu Chen,et al.  An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Andrea Monti Guarnieri,et al.  Distributed Scatterer Interferometry With the Refinement of Spatiotemporal Coherence , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Xiaolei Lv,et al.  A Review of Time-Series Interferometric SAR Techniques: A Tutorial for Surface Deformation Analysis , 2020, IEEE Geoscience and Remote Sensing Magazine.

[9]  M. Xing,et al.  InSAR Phase Denoising: A Review of Current Technologies and Future Directions , 2020, IEEE Geoscience and Remote Sensing Magazine.

[10]  Bangsen Tian,et al.  A Ground Surface Deformation Monitoring InSAR Method Using Improved Distributed Scatterers Phase Estimation , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Veronica Tofani,et al.  Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[12]  Jordi J. Mallorqui,et al.  SMF-POLOPT: An Adaptive Multitemporal Pol(DIn)SAR Filtering and Phase Optimization Algorithm for PSI Applications , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Richard Bamler,et al.  Efficient Phase Estimation for Interferogram Stacks , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[14]  J. Gong,et al.  Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: a case study of Jiaju landslide in Danba, China. , 2018 .

[15]  Batuhan Osmanoglu,et al.  Time Series Analysis of Insar Data: Methods and Trends , 2016 .

[16]  Hahn Chul Jung,et al.  A Phase-Decomposition-Based PSInSAR Processing Method , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Gianfranco Fornaro,et al.  CAESAR: An Approach Based on Covariance Matrix Decomposition to Improve Multibaseline–Multitemporal Interferometric SAR Processing , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Antonio Pepe,et al.  Improved EMCF-SBAS Processing Chain Based on Advanced Techniques for the Noise-Filtering and Selection of Small Baseline Multi-Look DInSAR Interferograms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Ramon F. Hanssen,et al.  Fast Statistically Homogeneous Pixel Selection for Covariance Matrix Estimation for Multitemporal InSAR , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Chao Wang,et al.  Phase Estimation for Distributed Scatterer INSAR:A comparison between different methods , 2014 .

[21]  Birsen Yazici,et al.  Joint-Scatterer Processing for Time-Series InSAR , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Xiaoli Ding,et al.  InSAR Coherence Estimation for Small Data Sets and Its Impact on Temporal Decorrelation Extraction , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

[26]  Dan Meng,et al.  A Novel Technique for Noise Reduction in InSAR Images , 2007, IEEE Geoscience and Remote Sensing Letters.

[27]  Xiaoli Ding,et al.  A Quantitative Measure for the Quality of INSAR Interferograms Based on Phase Differences , 2004 .

[28]  C. Werner,et al.  Interferometric point target analysis for deformation mapping , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

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

[30]  Jong-Sen Lee,et al.  Polarimetric SAR speckle filtering and its implication for classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[31]  F. Rocca,et al.  Permanent scatterers in SAR interferometry , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[32]  Jong-Sen Lee,et al.  Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery , 1994, IEEE Trans. Geosci. Remote. Sens..

[33]  Chang-Wook Lee,et al.  Improved Combined Scatterers Interferometry With Optimized Point Scatterers (ICOPS) for Interferometric Synthetic Aperture Radar (InSAR) Time-Series Analysis , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Mengyuan Wang,et al.  A Fast Phase Optimization Approach of Distributed Scatterer for Multitemporal SAR Data Based on Gauss–Seidel Method , 2022, IEEE Geoscience and Remote Sensing Letters.

[35]  Huifu Zhuang,et al.  An Improved Method for Phase Triangulation Algorithm Based on the Coherence Matrix Eigen-Decomposition in Time-Series SAR Interferometry , 2021, IEEE Access.

[36]  Oleksandr O. Bezvesilniy,et al.  EFFECTS OF LOCAL PHASE ERRORS IN MULTI-LOOK SAR IMAGES , 2013 .

[37]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..