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Journal of Applied Remote Sensing • Open Access

Unbiased-average minimum biased diffusion speckle denoising approach for synthetic aperture radar images
Author(s): Bing Sun; Jie Chen; Eric J. Tovar; Zhijun G. Qiao

Paper Abstract

Means of synthetic aperture radar (SAR) images represent the radiation densities of scenes, and the preservation of means is significant in speckle denoising for the application of SAR images. We provide an improved scheme of the minimum biased diffusion (MinBAD) algorithm for speckle denoising using partial differential equations. Considering the characteristics of SAR speckle and the radiation accuracy for postprocessing needs, several improvements such as normalization, homomorphic transformation, and average-preserving processing are introduced into the MinBAD algorithm. Besides the equivalent number of looks and edge preserving index, a new index, radiation accuracy error, is defined to evaluate the denoising effect. Experimental results for both artificial images and real SAR images are used to validate the performance of the proposed unbiased-average MinBAD speckle reducing approach.

Paper Details

Date Published: 28 April 2015
PDF: 13 pages
J. Appl. Remote Sens. 9(1) 095081 doi: 10.1117/1.JRS.9.095081
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Bing Sun, BeiHang Univ. (China)
Jie Chen, BeiHang Univ. (China)
Eric J. Tovar, The Univ. of Texas-Pan American (United States)
Zhijun G. Qiao, The Univ. of Texas-Pan American (United States)


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