Truncated-statistics-based bilateral filter for speckle reduction in synthetic aperture radar imagery

Jiaqiu Ai; Hang Yang; Xuezhi Yang; Ruiming Liu; Qiwu Luo; Xiaohui Zhang

doi:10.1117/1.JRS.13.026505

13 May 2019 Truncated-statistics-based bilateral filter for speckle reduction in synthetic aperture radar imagery

Jiaqiu Ai, Hang Yang, Xuezhi Yang, Ruiming Liu, Qiwu Luo, Xiaohui Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 026505 (May 2019). https://doi.org/10.1117/1.JRS.13.026505

Abstract

The mechanism of speckle noise in synthetic aperture radar (SAR) images and its characteristics are analyzed. Combining the advantages of the traditional bilateral filter (BF) and alpha-trimmed median filter, a truncated-statistics-based bilateral filter (TS-BF) in SAR imagery is proposed. The despeckling method is based on the BF methodology, where the similarities of gray levels and spatial location of the neighboring pixels are exploited. However, traditional BF is not effective to reduce the strong speckle, which is often presented as impulse noise. The proposed TS-BF filtering method designs an adaptive truncation method to properly select the samples in the local reference window, where the mean and standard deviation of all the samples are estimated, and the background types of the current pixel-for-filtering are categorized. Finally, the samples of the local reference window are truncated with different levels according to different background types, and BF is applied using the truncated samples. TS-BF can effectively preserve the edge and texture information of the image while smoothing the speckle noise; it has a great application value. The experimental results show the effectiveness of the proposed algorithm through subjective and objective analyses.

Citation Download Citation

Jiaqiu Ai, Hang Yang, Xuezhi Yang, Ruiming Liu, Qiwu Luo, and Xiaohui Zhang "Truncated-statistics-based bilateral filter for speckle reduction in synthetic aperture radar imagery," Journal of Applied Remote Sensing 13(2), 026505 (13 May 2019). https://doi.org/10.1117/1.JRS.13.026505

Received: 26 January 2019; Accepted: 18 April 2019; Published: 13 May 2019

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