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SAR Image Segmentation Using the Roughness Information
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DOI：10.1109/LGRS.2015.2496340.png)
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Abstract
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Image segmentation can be applied to a broad class of different problems. However, it is not usually a simple task for synthetic aperture radar (SAR) images due to the presence of speckle. Given the importance of SAR images in remote sensing problems, this letter introduces a simplified and general methodology to achieve SAR image segmentation by using the estimated roughness parameters of SAR data modeled by G(I)(0) and G(A)(0) distributions, instead of directly processing the speckled images. In this letter, we adopted the log-cumulants method for the roughness parameter estimation. The performance evaluation of the results was attained in terms of the error of segmentation and cross-region fitting measures for synthetic and real SAR images, respectively. With regard to synthetic images, we performed Monte Carlo experiments which confirmed the suitability of SAR image segmentation by means of roughness parameters. The results showed that the methodology provides a feasible input to SAR image segmentation algorithms which also include thresholding-based methods. The proposed approach accomplished satisfactory results for the most interesting and critical study case, i.e., the single-look images, which are markedly affected by speckle.
Keywords:
Log cumulants
roughness parameter
segmentation
synthetic aperture radar (SAR) image
thresholding
G(A)(0) distribution
G(I)(0) distribution
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