Bias Removal for Goldstein Filtering Power Using a Second Kind Statistical Coherence Estimator
Abstract
:1. Introduction
2. Methodology
2.1. Adaptive Goldstein Filtering
2.2. Coherence Estimation Based on the Second Kind Statistic
2.2.1. Definition of the Coherence Estimator
2.2.2. Weight Determination Weight
2.2.3. Bias Removal Using Second Kind Statistic Estimator
2.3. Modeling Filtering Power Using Unbiased Coherence
2.4. Filter Development
- (1)
- For each image pixel P, a coherence estimate window of size n is defined. The similarity between P and its neighbors is compared individually on an average intensity SAR image with patch size m (Figure 1a), and the weight of the coherence estimate window is confirmed using Equation (6). The sample coherence for pixel P is then estimated using Equation (3).
- (2)
- The procedure is repeated until the sample coherence of the last pixel in the whole image is estimated and coherence map is generated.
- (3)
- Goldstein filtering is performed on the interferogram in which the coherence sample with size k in each filtering patch is first averaged to obtain using Equation (11), and is further corrected to unbiased coherence according to Equation (12).
- (4)
- The filtering power is adjusted according to Equation (13) and Goldstein filtering is performed on such patches.
- (5)
- Steps (3) and (4) are repeated until the whole interferogram has been filtered.
3. Results and Discussion
3.1. Synthetic Data
3.2. Real Data
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Sensor | Track | Acquisition Date | Perp. Baseline | Imaging Area | Interferometric Feature | Mean Coherence |
---|---|---|---|---|---|---|
Sentinel-1A | 128 | 30 July 2017 | 35 m | Jiuzhaigou, China | Incoherent | 0.25 |
11 August 2017 | ||||||
TanDEM-X | - | 1 January 2013 | 286 m | Hong Kong, China | Coherent | 0.76 |
1 January 2013 |
Method | Residue Number | Residue Reduction (%) | |
---|---|---|---|
Unfiltered | 2.88 | 393,230 | - |
Baran | 1.80 | 136,828 | 65.20 |
AGFC | 1.72 | 125,197 | 68.16 |
AGFP | 1.05 | 17,647 | 95.51 |
New | 1.35 | 94,460 | 75.98 |
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Tian, X.; Jiang, M.; Xiao, R.; Malhotra, R. Bias Removal for Goldstein Filtering Power Using a Second Kind Statistical Coherence Estimator. Remote Sens. 2018, 10, 1559. https://doi.org/10.3390/rs10101559
Tian X, Jiang M, Xiao R, Malhotra R. Bias Removal for Goldstein Filtering Power Using a Second Kind Statistical Coherence Estimator. Remote Sensing. 2018; 10(10):1559. https://doi.org/10.3390/rs10101559
Chicago/Turabian StyleTian, Xin, Mi Jiang, Ruya Xiao, and Rakesh Malhotra. 2018. "Bias Removal for Goldstein Filtering Power Using a Second Kind Statistical Coherence Estimator" Remote Sensing 10, no. 10: 1559. https://doi.org/10.3390/rs10101559
APA StyleTian, X., Jiang, M., Xiao, R., & Malhotra, R. (2018). Bias Removal for Goldstein Filtering Power Using a Second Kind Statistical Coherence Estimator. Remote Sensing, 10(10), 1559. https://doi.org/10.3390/rs10101559