Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data
Phase unwrapping (PU) is a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (SAR, InSAR) data. In this paper, an improved PU method that combines an amended matrix pencil model, an adaptive unsce...
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2018-06-01
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author | Yandong Gao Shubi Zhang Tao Li Qianfu Chen Shijin Li Pengfei Meng |
author_facet | Yandong Gao Shubi Zhang Tao Li Qianfu Chen Shijin Li Pengfei Meng |
author_sort | Yandong Gao |
collection | DOAJ |
description | Phase unwrapping (PU) is a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (SAR, InSAR) data. In this paper, an improved PU method that combines an amended matrix pencil model, an adaptive unscented kalman filter (AUKF), an efficient quality-guided strategy based on heapsort, and a circular median filter is proposed. PU theory and the existing UKFPU method are covered. Then, the improved method is presented with emphasis on the AUKF and the circular median filter. AUKF has been well used in other fields, but it is for the first time applied to interferometric images PU, to the best of our knowledge. First, the amended matrix pencil model is used to estimate the phase gradient. Then, an AUKF model is used to unwrap the interferometric phase based on an efficient quality-guided strategy based on heapsort. Finally, the key results are obtained by filtering the results using a circular median. The proposed method is compared with the minimum cost network flow (MCF), statistical cost network flow (SNAPHU), regularized phase tracking technique (RPTPU), and UKFPU methods using two sets of simulated data and two sets of experimental GF-3 SAR data. The improved method is shown to yield the greatest accuracy in the interferometric phase maps compared to the methods considered in this paper. Furthermore, the improved method is shown to be the most robust to noise and is thus most suitable for PU of GF-3 SAR data in high-noise and low-coherence regions. |
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spelling | doaj.art-abbc9087c6234a329f3221bf25aafea52022-12-22T03:19:30ZengMDPI AGSensors1424-82202018-06-01186179310.3390/s18061793s18061793Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR DataYandong Gao0Shubi Zhang1Tao Li2Qianfu Chen3Shijin Li4Pengfei Meng5School of Environment Science and Spatial Informatics, China University of Miningand Technology, Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Miningand Technology, Xuzhou 221116, ChinaSatellite Surveying and Mapping Application Center, National Administration of Surveying, Mapping and Geo-Information, Haidian District, Beijing 100048, ChinaSatellite Surveying and Mapping Application Center, National Administration of Surveying, Mapping and Geo-Information, Haidian District, Beijing 100048, ChinaSchool of Environment Science and Spatial Informatics, China University of Miningand Technology, Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Miningand Technology, Xuzhou 221116, ChinaPhase unwrapping (PU) is a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (SAR, InSAR) data. In this paper, an improved PU method that combines an amended matrix pencil model, an adaptive unscented kalman filter (AUKF), an efficient quality-guided strategy based on heapsort, and a circular median filter is proposed. PU theory and the existing UKFPU method are covered. Then, the improved method is presented with emphasis on the AUKF and the circular median filter. AUKF has been well used in other fields, but it is for the first time applied to interferometric images PU, to the best of our knowledge. First, the amended matrix pencil model is used to estimate the phase gradient. Then, an AUKF model is used to unwrap the interferometric phase based on an efficient quality-guided strategy based on heapsort. Finally, the key results are obtained by filtering the results using a circular median. The proposed method is compared with the minimum cost network flow (MCF), statistical cost network flow (SNAPHU), regularized phase tracking technique (RPTPU), and UKFPU methods using two sets of simulated data and two sets of experimental GF-3 SAR data. The improved method is shown to yield the greatest accuracy in the interferometric phase maps compared to the methods considered in this paper. Furthermore, the improved method is shown to be the most robust to noise and is thus most suitable for PU of GF-3 SAR data in high-noise and low-coherence regions.http://www.mdpi.com/1424-8220/18/6/1793phase unwrappingGaofen-3 SARAUKFcircular median filterrobustness |
spellingShingle | Yandong Gao Shubi Zhang Tao Li Qianfu Chen Shijin Li Pengfei Meng Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data Sensors phase unwrapping Gaofen-3 SAR AUKF circular median filter robustness |
title | Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data |
title_full | Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data |
title_fullStr | Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data |
title_full_unstemmed | Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data |
title_short | Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data |
title_sort | adaptive unscented kalman filter phase unwrapping method and its application on gaofen 3 interferometric sar data |
topic | phase unwrapping Gaofen-3 SAR AUKF circular median filter robustness |
url | http://www.mdpi.com/1424-8220/18/6/1793 |
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