1. Troy P. Kling Mentors: Dr. Maxim Neumann, Dr. Razi Ahmed
Statistical Image Filtering and Denoising Techniques for Synthetic Aperture Radar Data
Troy P. Kling
Mentors: Dr. Maxim Neumann, Dr. Razi Ahmed

2. Outline Introduction Non-Local Filters Continuing & Future Research
Description and example of speckle noise
Overview of local Filters (Boxcar and Lee)
Non-Local Filters
Buades’ Non-local means filter
Deledalle’s NL-InSAR filter
Continuing & Future Research
The new multi-baseline NL-InSAR filter
NL-PolSAR for polarimetric data
Randomized non-local means filter
Edge detection, object classification, and computer vision

3. Speckle Noise
Synthetic aperture radar (SAR) is inherently affected by speckle noise.
Speckle can be modeled by a circular complex Gaussian distribution:
Random walk that generates a resultant complex value, i.e. multiplicative speckle noise.

4. Speckle Noise
Left: Google Earth image of a golf course in Harvard Forest, Massachusetts.
Right: UAVSAR image of the same golf course. Speckle noise is very apparent.

5. Local Filters Boxcar filter J. S. Lee’s filter Local noise reduction
Moving average
J. S. Lee’s filter
Adaptive noise reduction
Uses directional masks
Adaptive filtering coefficient, k, quantifies local homogeneity
Eight directional masks used in Lee’s filter.

6. Top left: Original image
Top left: Original image. Top right: Image with Gaussian white noise added. Bottom left: 7x7 Boxcar filter. Bottom right: 7x7 Lee filter.

7. Non-Local Filters – NL-Means
Considers all pixels in the image, and performs a weighted average:
Better at preserving textures and fine structures than most local speckle filters.

8. Non-Local Filters – NL-Means

9. Non-Local Filters – NL-InSAR
Non-Local Means applied to interferometric SAR (InSAR) images
Uses a more statistically-grounded similarity criterion than NL-means
Estimates reflectivity, phase, and coherence simultaneously using weighted maximum likelihood estimation
Applied iteratively

10. Non-Local Filters – NL-InSAR
Left: Google Earth image of a golf course in Harvard Forest, Massachusetts.
Right: UAVSAR image of the same golf course. Speckle noise is very apparent.

11. Non-Local Filters – NL-InSAR
Left: Estimated reflectivity after 1 iteration. Search window of size 35x35 and similarity window of size 7x7 were used. Right: Estimated reflectivity after 10 iterations.

12. Non-Local Filters – NL-InSAR
Left: Estimated phase after 1 iteration. Search window of size 35x35 and similarity window of size 7x7 were used. Right: Estimated phase after 10 iterations.

13. Non-Local Filters – NL-InSAR
Left: Estimated coherence after 1 iteration. Search window of size 35x35 and similarity window of size 7x7 were used. Right: Estimated coherence after 10 iterations.

14. Continuing Research Multiple Baseline NL-InSAR
Extending NL-InSAR to work with more than two SLC images
Requires estimating the phase and coherence between several pairs of SLC images
Similarity likelihood derivation becomes complicated very quickly

15. Future Research NL-PolSAR filter NL-MC filter Edge Detection
Modifying NL-InSAR to work with polarimetry
Applications to land cover type classification
NL-MC filter
Adding randomness (Monte Carlo methods) to make the NL-means algorithm truly non-local
Edge Detection
Using image filters to improve edge detection and object classification in computer vision

16. References
J. S. Lee, M. R. Grunes, G. de Grandi, "Polarimetric SAR Speckle Filtering and Its Implications for Classification", IEEE Transactions on Geoscience and Remote Sensing, pp
X. X. Zhu, R. Bamler, M. Lachaise, F. Adam, Y. Shi, and M. Eineder, "Improving TanDEM-X DEMs by Non-Local InSAR Filtering", European Conference on Synthetic Aperture Radar, pp J. S. Lee, "Digital Image Enhancement and Noise Filtering by Use of Local Statistics", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp
J. S. Lee, "Refined Filtering of Image Noise Using Local Statistics", Computer Graphics and Image Processing, pp
C. Deledalle, L. Denis, F. Tupin, "NL-InSAR: Non-Local Interferogram Estimation", IEEE Transactions on Geoscience and Remote Sensing, pp
A. Buades, B. Coll, and J. M. Morel, "Image Denoising Methods. A New Nonlocal Principle", Society for Industrial and Applied Mathematics, pp
C. Deledalle, L. Denis, F. Tupin, A. Reigber, and M. Jager, "NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising", pp
N. Goodman, "Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution (an Introduction)", Annals of Mathematical Statistics, pp
"Speckle Filtering", The Polarimetric SAR Data Processing and Educational Tool, pp