[english]
[français]

NL-InSAR: Non-Local Interferogram Estimator



Description of the filter

up

Qualitative Evaluation of the Denoising Algorithms

smalltoulouse: slc

 
smalltoulouse: nlinsar

  (left) Reflectivity, (center) phase difference and (right) coherence of Toulouse (RAMSES, under one meter) ©DGA ©ONERA obtained from top to bottom by the SLC SAR images [1], the 10 iterations of NL-InSAR.
mire: slc

 
mire: nlinsar

  (left) Reflectivity, (center) phase difference and (right) coherence of Resolution test pattern (Synthetic) obtained from top to bottom by the SLC SAR images [1], the 10 iterations of NL-InSAR.

Show more comparisons and results

Filters:
Images:

Thanks to the CNES for providing the RAMSES data and to the ANR for providing the TerraSAR-X data in the framework of the EFIDIR project.

up

Quantitative Evaluation of the Denoising Algorithms

toulouse: insar_bc7x7

 
toulouse: insar_lee

 
toulouse: insar_idan

 
toulouse: insar_nlinsar_lik

 
toulouse: insar_nlinsar
Statistical answer on a rectangular function obtained from top to bottom by the boxcar estimator, Lee's estimator [2], the IDAN estimator [3], the non-iterative NL-InSAR estimator and the (iterative) NLInSAR estimator.
TABLE
SNR Values of Estimated InSAR Images Using Different Estimators and the Computation Time
Refl. Phase Cohe. Time (sec)
SLC Images -2.753.36-1.19-
WIN-SAR 5.90--101.76
PEARLS -5.27-394.83
Boxcar filter 6.475.90-4.010.22
Lee 6.239.122.300.77
IDAN 5.007.880.33522.53
NL-InSAR (non-it) 6.268.705.82148.39
NL-InSAR (10 it.) 9.0213.046.921540.93
up

NL-InSAR software

Download the NL-InSAR estimator

We recommend to use the more recent NL-SAR technique for speckle noise reduction (available here: NL-SAR)

 

These pieces of Matlab softwares are based on C++ Mex-Functions compiled for Linux 32-bit, Linux 64-bit and Windows 32 bit. Matlab script exemples are given, they have been written for MATLAB with the Image Processing Toolbox (to load the images). Please refer to the REAME file for more details. For any comment, suggestion or question please contact Charles-Alban Deledalle at deledalle (at) telecom-paristech (dot) fr.

up

References

  1. M. Seymour and I. Cumming,
    Maximum likelihood estimation for SAR interferometry,
    In the 1994 International Geoscience and RemoteSensing Symposium., vol. 4, pp. 2272-2274, 1994.
  2. J. Lee, S. Cloude, K. Papathanassiou, M. Grunes, and I. Woodhouse,
    Speckle filtering and coherence estimation of polarimetric SAR interferometry data for forest applications,
    IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 10 Part 1, pp. 2254-2263, 2003.
  3. G. Vasile, E. Trouvé , J. Lee, and V. Buzuloiu,
    Intensity-Driven Adaptive-Neighborhood Technique for Polarimetric and Interferometric SAR Parameters Estimation,
    IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6, p. 1609-1621, 2006.
  4. A. Achim, P. Tsakalides, and A. Bezerianos,
    SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling,
    IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 8, pp. 1773-1784, 2003.
  5. J. Bioucas-Dias, V. Katkovnik, J. Astola, and K. Egiazarian,
    Absolute phase estimation: adaptive local denoising and global unwrapping,
    Applied Optics, vol. 47, no. 29, pp. 5358-5369, 2008.
  6. Charles-Alban Deledalle, Loïc Denis and Florence Tupin,
    Iterative Weighted Maximum Likelihood Denoising with Probabilistic Patch-Based Weights,
    IEEE Trans. on Image Processing, vol. 18, no. 12, pp. 2661-2672, December 2009 (download)
  7. Buades, A. and Coll, B. and Morel, J.M.
    A Non-Local Algorithm for Image Denoising,
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2005
up

Last modified: Fri Aug 23 15:52:33 Europe/Berlin 2019