Speckle reduction of SAR images using adaptive regularized least square support vector machines

Daiqiang Peng; Jinwen Tian; Jian Liu

doi:10.1117/12.750579

15 November 2007 Speckle reduction of SAR images using adaptive regularized least square support vector machines

Daiqiang Peng, Jinwen Tian, Jian Liu

Proceedings Volume 6787, MIPPR 2007: Multispectral Image Processing; 67871Z (2007) https://doi.org/10.1117/12.750579
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposed an adaptive regularized approach to reduce SAR image speckle based on least squares support vector machines (LS-SVM). Generally, SAR images are comprised of multiple features of different spatial scales, and there is typically a trade-off between speckle removal and detail preservation. A natural approach to partially alleviate this problem is to use spatial adaptive regularization parameter on the use of regularized procedure. Here, each pixel has its own associated regularization parameter in this paper, instead of choosing a global regularization parameter. Experimental results show that our approach has a good performance on the speckle reduction without destruction of important SAR image details.

Citation Download Citation

Daiqiang Peng, Jinwen Tian, and Jian Liu "Speckle reduction of SAR images using adaptive regularized least square support vector machines", Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67871Z (15 November 2007); https://doi.org/10.1117/12.750579

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KEYWORDS

Synthetic aperture radar

Speckle

Image filtering

Digital filtering

Wavelets

Image denoising

Image processing

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