Transcript Statistical Image Filtering and Denoising Techniques for

Statistical Image Filtering and
Denoising Techniques for
Synthetic Aperture Radar Data
Troy P. Kling
Mentors: Dr. Maxim Neumann, Dr. Razi Ahmed
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
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The new multi-baseline NL-InSAR filter
NL-PolSAR for polarimetric data
Randomized non-local means filter
Edge detection, object classification, and computer vision
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.
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.
Local Filters
• Boxcar filter
– Local noise reduction
– Moving average
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• 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.
Top left: Original image. Top right: Image with Gaussian white noise
added. Bottom left: 7x7 Boxcar filter. Bottom right: 7x7 Lee filter.
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.
Non-Local Filters – NL-Means
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
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.
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.
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.
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.
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
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
References
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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.
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.
J. S. Lee, "Refined Filtering of Image Noise Using Local Statistics", Computer Graphics and Image
Processing, pp. 380-389. 1981.
C. Deledalle, L. Denis, F. Tupin, "NL-InSAR: Non-Local Interferogram Estimation", IEEE
Transactions on Geoscience and Remote Sensing, pp. 1-11. 2010.
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.
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.
N. Goodman, "Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution
(an Introduction)", Annals of Mathematical Statistics, pp. 152-177. 1963.
"Speckle Filtering", The Polarimetric SAR Data Processing and Educational Tool, pp. 1-12. 2011.