SAR image despeckling using undecimated directional filter banks and mean shift

Authors:
Gong Zhang;Wenhua Shi;Jing Xu;Ning Li;Mark J. T. Smith
Affiliations:
College of Information Science and Technology, Nanjing Univ. of Aeronautics and Astronautics, Nanjing, China. P.R.C;College of Information Science and Technology, Nanjing Univ. of Aeronautics and Astronautics, Nanjing, China. P.R.C;College of Information Science and Technology, Nanjing Univ. of Aeronautics and Astronautics, Nanjing, China. P.R.C;College of Information Science and Technology, Nanjing Univ. of Aeronautics and Astronautics, Nanjing, China. P.R.C;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery can make it difficult to visually and automatically interpret SAR data. Speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, a novel method of SAR image despeckling is presented that uses undecimated directional filter banks (UDFB) and mean shift clustering. The UDFB is obtained by manipulating the resampling matrices in the Bamberger directional filter banks (DFB), such that low computational complexity is preserved, while achieving shift invariance that could be useful in pattern recognition and image denoising applications. A nonparametric estimator of the density gradient is employed in the joint spatial-range domain of the directional bands obtained by the UDFB. Examples included at the end of the paper illustrate typical performance results obtained using this method.