Interferometric phase filtering represents an indispensable step in interferometric synthetic aperture radar (InSAR) data processing. However, conventional filtering methods fail to make a balance between the noise elimination and phase preserving in strong noise environments or dense fringe regions. This in turn leads to an inaccurate interferometric phase. To overcome this problem, the authors introduce the concept of slope-compensated filter based on the local adaptive frequency estimation. This method uses the estimation of signal parameters via rotational invariance techniques (ESPRITs), based on the singular value decomposition of the correlation matrix and generalised eigenvalue solution for providing an accurate local fringe frequency. Meanwhile, the size of the estimation window is adaptively determined by the coherence coefficient, and the judgement and modification of the invalid frequency estimation are utilised to further improve the accuracy of the local fringe frequency. Moreover, the authors consider a two-stage with the back projection technique to further eliminate the noise residual and improve the filtering performance. The simulated and TerraSAR-X add-on for Digital Elevation Measurements (TanDEM) data sets are used to verify the effectiveness of the proposed method. The experimental results show that this method is capable of efficiently reducing the noise level as well as minimising the loss of the signal, outperforming other conventional methods.

References

1. 1)
  - 6. Buades, A., Coll, B., Morel, J.M.: ‘A non-local algorithm for image denoising’. IEEE Computer Society Conf. on Computer Vision & Pattern Recognition IEEE Computer Society, San Diego, USA, 2005, pp. 60–65.
2. 2)
  - 19. Xue, H., Feng, D.: ‘New locally adaptive method for InSAR phase noise filtering’, J. Electron. Inf. Technol., 2016, 38, (12), pp. 3085–3092.
3. 3)
  - 7. Deledalle, C.A., Denis, L., Tupin, F.: ‘Iterative weighted maximum likelihood denoising with probabilistic patch-based weights’, IEEE Trans. Image Process., 2009, 18, (12), pp. 2661–2672.
4. 4)
  - 4. Yi, H., Zhu, J., Xu, B., et al: ‘A filter based on weighting by anisotropic gradient for interferogram’, J. Central South Univ., 2014, 45, (8), pp. 2708–2715.
5. 5)
  - 22. Guo, Q., Zhang, C., Zhang, Y., et al: ‘An efficient SVD-based method for image denoising’, IEEE Trans. Circuits Syst. Video Technol., 2016, 26, (5), pp. 868–880.
6. 6)
  - 12. Zhao, C., Zhang, Q., Ding, X., et al: ‘An iterative Goldstein SAR interferogram filter’, Int. J. Remote Sens., 2012, 33, (11), pp. 3443–3455.
7. 7)
  - 15. Trouvé, E., Nicolas, J.-M., Maître, H.: ‘Improving phase unwrapping techniques by the use of local frequency estimates’, IEEE Trans. Geosci. Remote Sens., 1998, 36, (6), pp. 1963–1972.
8. 8)
  - 3. Frost, V.S., Stiles, J.A., Shanmugan, K.S., et al: ‘A model for radar images and its application to adaptive digital filtering of multiplicative noise’, IEEE Trans. Pattern Anal. Mach. Intell., 1982, 4, (2), pp. 157–166.
9. 9)
  - 5. Wang, B.H., Zhao, C.Y., Liu, Y.Y.: ‘An improved SAR interferogram denoising method based on principal component analysis and the goldstein filter’, Remote Sens. Lett., 2018, 9, (1), pp. 81–90.
10. 10)
  - 9. Chierchia, G., Gheche, M.E., Scarpa, G., et al: ‘Multitemporal SAR image despeckling based on block-matching and collaborative filtering’, IEEE Trans. Geosci. Remote Sens., 2017, PP, (99), pp. 1–14.
11. 11)
  - 14. Spagnolini, U.: ‘2-D phase unwrapping and instantaneous frequency estimation’, IEEE Trans. Geosci. Remote Sens., 1995, 33, (3), pp. 579–589.
12. 12)
  - 1. Li, H., Liao, G.: ‘An estimation method for InSAR interferometric phase based on MMSE criterion’, IEEE Trans. Geosci. Remote Sens., 2010, 48, (3), pp. 1457–1469.
13. 13)
  - 13. Jiang, M., Ding, X., Li, Z., et al: ‘The improvement for Baran phase filter derived from unbiased InSAR coherence’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (7), pp. 3002–3010.
14. 14)
  - 8. Zhang, W., Zhang, Q., Zhao, C., et al: ‘Noise reduction for InSAR phase images using BM3D’, Chin. J. Electron., 2014, 23, (2), pp. 329–333.
15. 15)
  - 2. Lee, J.S., Papathanassiou, K.P., Ainsworth, T.L., et al: ‘A new technique for noise filtering of SAR interferometric phase images’, IEEE Trans. Geosci. Remote Sens., 1998, 36, (5), pp. 1456–1465.
16. 16)
  - 11. Baran, I., Stewart, M.P., Kampes, B.M., et al: ‘A modification to the Goldstein radar interferogram filter’, IEEE Trans. Geosci. Remote Sens., 2003, 41, (9), pp. 2114–2118.
17. 17)
  - 16. Huang, B., Xu, J.: ‘A locally adaptive filter for noise filtering of InSAR interferogram’. Int. Conf. on Intelligent Networks and Intelligent Systems IEEE, Wuhan, China, 2008, pp. 573–576.
18. 18)
  - 20. Suo, Z., Li, Z., Bao, Z.: ‘A new strategy to estimate local fringe frequencies for InSAR phase noise reduction’, IEEE Geosci. Remote Sens. Lett., 2010, 7, (4), pp. 771–775.
19. 19)
  - 21. Cao, Z.: ‘A new method for estimating 2-D DOA in coherent source environment with two parallel linear array’, Radar Sci. Technol., 2003, 1, (2), pp. 104–108.
20. 20)
  - 17. Cai, B., Liang, D., Dong, Z.: ‘A new adaptive multiresolution noise-filtering approach for SAR interferometric phase images’, IEEE Geosci. Remote Sens. Lett., 2008, 5, (2), pp. 266–270.
21. 21)
  - 18. Suo, Z., Li, M., Zhang, Q., et al: ‘InSAR phase noise filter in frequency domain’, IEEE Trans. Geosci. Remote Sens., 2016, 54, (2), pp. 1185–1195.
22. 22)
  - 10. Goldstein, R.M., Werner, C.L.: ‘Radar interferogram filtering for geophysical applications’, Geophys. Res. Lett., 1998, 25, (21), pp. 4035–4038.

Slope-compensated interferogram filter with ESPRIT for adaptive frequency estimation

References

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