Istanbul 2004
Edge Keeping
16)
donated the
ered image in
of the sample
——— PG sq
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
(g) LogMean filter
Fig 2 the contrast test results
The following table is the results of quantitative evalution.
Table 1 the results of quantitative evaluation
2
ß FI ENL NM EKI
Original 0.240 | 4.162 4.732 1.000 | 1.000
Mean 0.137]. 2.308 14.571 | 1.031 | 0.464
Median 0.158 | 6.344 10.996 | 1.047 | 0.525
Enh Lee 0.138 | 7.266 14.424 | 1.033 | 0.613
filter
EnhFrost 0.133 | 7.527 15.479 | 1.043 | 0.627
filter
GammaMAP | 0.133 | 7.502 15.375 | 1.041 | 0.638
filter
logMean 0.139 | 7.194 14.140 | 0.867 | 0.607
7. Conclusion
The most well-known adaptive filters for SAR images are
analyzed.From the above contrast test, we can see that the
enhanced Lee filter! lenhanced Frost filter and Gamma MAP
filter not only restrain the speckle noise very well, but also
preserve the edge and texture information. We can also choose
appropriate thresholds to enhance the adaptive filters.
REFERENCE
J.S.Lee, Digital image enhancement and noise filtering by use
of local statistics, IEEE Trans. On Pattern Analysis and Machine
Intelligence, 1980
J.S. Lee, Refined filtering of image noise using local statistics,
Computer Vision, Graphics, and Image Processing, 1981
Lopes,R.Touzi,and E.Nezry, ” Adaptive Speckle Filters and
Scene Heterogeneity ”,IEEE Trans. On Geoscience and Remote
Sensing, vol.28, No6, pp992-1000,Nov.1990
Lopes A, Touzi R,Laur H. Structure Detection and Statistical
Adaptive Speckle Filtering in SAR Images. International
Journal of Remote Sensing, 1993, 14(9), 1735-1758
Martin F J, Turner R W. SAR Speckle Reduction by Weighted
Filtering. International Journal of Remote Sensing,
1993,14:1759-1774
Smith D M. Speckle Reduction and Segment of Synthetic
Aperture Radar Images. International Journal of Remote
Sensing, 1996,17(11):2043-2057
