DocumentCode :
1549386
Title :
Filtering of multichannel SAR images
Author :
Quegan, Shaun ; Yu, Jiong Jiong
Author_Institution :
Centre for Earth Obs. Sci., Univ. of Sheffield, UK
Volume :
39
Issue :
11
fYear :
2001
fDate :
11/1/2001 12:00:00 AM
Firstpage :
2373
Lastpage :
2379
Abstract :
An explicit form of the linear multichannel synthetic aperture radar (SAR) intensity filter, which preserves radiometry while optimally reducing speckle is derived, together with a compact expression for the theoretical gain in equivalent numbers of looks (ENLs). The filter can be applied to mixed data types, which is demonstrated using a combination of ERS and JERS satellite data, and confirms the filter performance predicted by the theory. Tests indicate that a simplified form of the filter, which neglects correlation between images, gives an ENL only slightly less than optimal, while being much easier to implement. Exact analysis of the effect of estimating filter weights shows that the linear increase in ENL with the number of images predicted for the ideal filter does not occur. In practice, the ENL is affected by the window size used to estimate the weights and saturates as the number of images increases. An efficient recursive form of the filter is described, which is most naturally applied to multitemporal data for the practically important case where the current image is uncorrelated with previous images in a data sequence
Keywords :
agriculture; forestry; remote sensing by radar; synthetic aperture radar; vegetation mapping; ERS data; JERS data; SAR data; agriculture; equivalent number of looks; filter performance; forestry; linear multichannel synthetic aperture radar intensity filter; mixed data types; multichannel SAR images; multitemporal data; multitemporal filtering; optimally reducing speckle; radiometry; satellite data; speckle reduction; Data mining; Filtering theory; Forestry; Image analysis; Nonlinear filters; Radiometry; Satellite broadcasting; Speckle; Synthetic aperture radar; Testing;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/36.964973
Filename :
964973
Link To Document :
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