An Interferogram ReFlattening Method for InSAR Based on Local Residual Fringe Removal and Adaptively Adjusted Windows
Abstract
:1. Introduction
2. Related Work
2.1. Global Refinement and ReFlattening Methods
2.1.1. Polynomial Refinement Method
2.1.2. Refinement and ReFlattening Based on Baseline Correction
2.2. Flattening or ReFlattening Methods Based on Manually Set Windows
3. Methods
3.1. Characteristics of Residual Fringes Caused by Baseline Errors
 The residual fringes conform to the first or seconddegree polynomial phase model locally;
 Residual fringes are time varying in azimuth;
 The time varying of residual fringes is irregular in the whole image.
3.2. The Proposed Method: A ReFlattening Method Based on Local Residual Fringe Removal and Adaptively Adjusted Windows
3.2.1. Principle of the ReFlattening within A Local Window
3.2.2. Mechanism of Adaptive Adjustment for ReFlattening Windows
4. Experiments
4.1. Experimental Data and Study Area
4.2. ReFlattening Results and Qualitative Evaluations
4.3. DEM Generation and Quantitative Evaluation
5. Discussion
5.1. The Influence of Coherence on the Performance of the Proposed ReFlattening Method
5.2. Comparison with the Result Based on TerraSARX Data with Similar Conditions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Methods  Strengths  Weaknesses  

Global refinement and reflattening methods  polynomial refinement method 


reflattening method based on baseline correction 

 
reflattening methods based on manually set windows 

 
the proposed method 


Imaging Mode  Polarization  Resolution (Azimuth × Slant Range)  2π Ambiguity Height  

GF3 InSAR data in Ningbo  Fine Strip I  HH  2.86 × 3.31 m  31.05 m 
GF3 InSAR data in Yutian  Fine Strip I  HH  3.13 × 3.61 m  62.55 m 
GF3 InSAR data in Xi’an  QuadPolarization Strip I  HH  5.54 × 3.80 m  109.98 m 
Sentinel1A InSAR data in Yancheng  IW  HH  13.92 × 23.30 m  224.12 m 
Data\Indictor  Average Coherence  $2\mathit{\pi}$ Ambiguity Height (m)  Method  MAE (m)  RMSE (m) 

GF3 InSAR data (Ningbo City)  0.35  31.05  the proposed method  9.84  15.17 
GPR method  66.60  90.60  
SBDR method  68.65  89.45  
LFEMW method  29.52  40.21  
GF3 InSAR data(Yutian County)  0.24  62.55  the proposed method  11.27  17.86 
GPR method  132.18  166.45  
SBDR method  136.54  172.56  
LFEMW method  22.72  27.37  
GF3 InSAR data (Xi’an City)  0.46  109.98  the proposed method  32.92  53.95 
GPR method  123.18  206.91  
SBDR method  287.50  332.19  
LFEMW method  81.03  107.25  
Sentinel1A InSAR data (Yancheng City)  0.49  224.12  the proposed method  33.15  41.01 
GPR method  62.84  76.48  
SBDR method  55.00  65.61  
LFEMW method  111.22  137.95 
Data\Indicator  Average Coherence  Ambiguity Height (m)  MAE (m)  RMSE (m) 

GF3 InSAR data in Ningbo  0.35  31.05  9.84  15.17 
TerraSARX InSAR data  0.47  48.88  11.22  13.35 
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Zhuang, D.; Zhang, L.; Zou, B. An Interferogram ReFlattening Method for InSAR Based on Local Residual Fringe Removal and Adaptively Adjusted Windows. Remote Sens. 2023, 15, 2214. https://doi.org/10.3390/rs15082214
Zhuang D, Zhang L, Zou B. An Interferogram ReFlattening Method for InSAR Based on Local Residual Fringe Removal and Adaptively Adjusted Windows. Remote Sensing. 2023; 15(8):2214. https://doi.org/10.3390/rs15082214
Chicago/Turabian StyleZhuang, Di, Lamei Zhang, and Bin Zou. 2023. "An Interferogram ReFlattening Method for InSAR Based on Local Residual Fringe Removal and Adaptively Adjusted Windows" Remote Sensing 15, no. 8: 2214. https://doi.org/10.3390/rs15082214