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Speckle Filtering of Polarimetric SAR Interferometry Data

Dr. Jong-Sen Lee (1), Dr. Shane Cloude(2) , Mitchell Grunes(1) , and Dr. Kostas Papathanassiou(3)

(1) NRL, 4555 Overlook Ave., SW, Washington DC, 20375-5351, United States
(2) AEL Consultants, Cupar,Fife, KY15 5AA, Scotland, United Kingdom
(3) DLR German Aerospace Center, DLR, D-82230, Oberphaffenhofen, Germany


Polarimetric SAR interferometry has generated great interest for forest applications. The complex interferometric coherence from combinations of polarizations is computed to form the 6x6 coherency matrix. This matrix is generally filtered by a boxcar filter to reduce speckle, and then the linear combination of polarizations is optimized producing three optimized coherences and their associated interferometric phases. In earlier work, the maximum phase difference between these three interferometric phases was used to estimate tree heights. Recently, a coherent mixture model of a random volume and ground proposed by Papathanassiou and Cloude [1] has been applied to extract tree heights and the extinction coefficient based on interferometric coherence for polarimetric SAR. In both approaches, the accurate estimation of coherence is critical for tree height estimation. Interferometric coherence is a statistical average. It requires averaging many samples from the same distribution. It has been shown that an insufficient number of samples would produce an overestimate of the true coherence. For example, for 1-look interferometric SAR data, the coherence is at the maximum value of 1. The second problem associated with coherence computation is that the coherence is underestimated when averaging samples from difference distributions. Thus, the commonly used boxcar filter would produce erroneous coherences especially near forest boundaries, or in inhomogeneous vegetated areas. For examples, at forest edges, a 5x5 window would contain samples from two distinct distributions. The indiscriminate average produces a lower coherence at the edges as dark lines (lower coherence) of 3 to 5 pixel wide.. Most forest areas are not homogeneous, such as those in the Glen Affric Project. Better speckle averaging than the boxcar filter is needed. The basic principle in speckle filtering of polarimetric interferometric data is in the selection of pixels of the same scattering characteristics to be included in the average. Polarimetric SAR has the capability of characterizing the scattering mechanism of a medium. The filtering is done on the 6x6 coherency matrix based on the speckle filter that minimize the mean square error [2]. The filtered image successfully corrects the deficiency of the boxcar filter. The problem of dark (low coherence) lines along forest edges is eliminated. Importance in the tree height extractions using the Glen Affric data will be demonstrated.


[1] K.P. Papathanassiou, S.R. Cloude and A. Reigbor, "Single and Multi-Baseline Polarimetric SAR Interferometry over Forested Terrain," Proceedings of EUSAR 2000, pp123-126, Munich, Germany.

[2] J.S. Lee et al., "Polarimetric SAR Speckle Filtering and Its Implication for Classification," IEEE TGRS, vol.37, no. 5, September 1999.


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