statistical image filtering and denoising techniques for synthetic aperture radar data troy p. kling...
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![Page 1: Statistical Image Filtering and Denoising Techniques for Synthetic Aperture Radar Data Troy P. Kling Mentors: Dr. Maxim Neumann, Dr. Razi Ahmed](https://reader035.vdocuments.us/reader035/viewer/2022062716/56649e145503460f94afe837/html5/thumbnails/1.jpg)
Statistical Image Filtering and Denoising Techniques for
Synthetic Aperture Radar Data
Troy P. KlingMentors: Dr. Maxim Neumann, Dr. Razi
Ahmed
![Page 2: Statistical Image Filtering and Denoising Techniques for Synthetic Aperture Radar Data Troy P. Kling Mentors: Dr. Maxim Neumann, Dr. Razi Ahmed](https://reader035.vdocuments.us/reader035/viewer/2022062716/56649e145503460f94afe837/html5/thumbnails/2.jpg)
Outline
• Introduction– 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
![Page 3: Statistical Image Filtering and Denoising Techniques for Synthetic Aperture Radar Data Troy P. Kling Mentors: Dr. Maxim Neumann, Dr. Razi Ahmed](https://reader035.vdocuments.us/reader035/viewer/2022062716/56649e145503460f94afe837/html5/thumbnails/3.jpg)
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.
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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.
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Local Filters
• Boxcar 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.
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Top left: Original image. Top right: Image with Gaussian white noise added. Bottom left: 7x7 Boxcar filter. Bottom right: 7x7 Lee filter.
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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.
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Non-Local Filters – NL-Means
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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
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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.
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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.
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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.
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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.
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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
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Future Research
• NL-PolSAR filter– 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
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References1. 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. 2363-2373. 1999.
2. 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. 1125-1128. 2014. J. S. Lee, "Digital Image Enhancement and Noise Filtering by Use of Local Statistics", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 165-168. 1980.
3. J. S. Lee, "Refined Filtering of Image Noise Using Local Statistics", Computer Graphics and Image Processing, pp. 380-389. 1981.
4. C. Deledalle, L. Denis, F. Tupin, "NL-InSAR: Non-Local Interferogram Estimation", IEEE Transactions on Geoscience and Remote Sensing, pp. 1-11. 2010.
5. A. Buades, B. Coll, and J. M. Morel, "Image Denoising Methods. A New Nonlocal Principle", Society for Industrial and Applied Mathematics, pp. 113-147. 2010.
6. 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. 1-17. 2014.
7. N. Goodman, "Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution (an Introduction)", Annals of Mathematical Statistics, pp. 152-177. 1963.
8. "Speckle Filtering", The Polarimetric SAR Data Processing and Educational Tool, pp. 1-12. 2011.
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