Semisupervised synthetic aperture radar image segmentation with multilayer superpixels

Can Wang; Weimin Su; Hong Gu; Dachen Gong

doi:10.1117/1.JRS.9.095098

14 January 2015 Semisupervised synthetic aperture radar image segmentation with multilayer superpixels

Can Wang, Weimin Su, Hong Gu, Dachen Gong

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095098 (January 2015). https://doi.org/10.1117/1.JRS.9.095098

Abstract

Image segmentation plays a significant role in synthetic aperture radar (SAR) image processing. However, SAR image segmentation is challenging due to speckle. We propose a semisupervised bipartite graph method for segmentation of an SAR image. First, the multilayer over-segmentation of the SAR image, referred to as superpixels, is computed using existing segmentation algorithms. Second, an unbalanced bipartite graph is constructed in which the correlation between pixels is replaced by the texture similarity between superpixels, to reduce the dimension of the edge matrix. To also improve efficiency, we define a new method, called the combination of the Manhattan distance and symmetric Kullback–Leibler divergence, to measure texture similarity. Third, by the Moore–Penrose inverse matrix and semisupervised learning, we construct an across-affinity matrix. A quantitative evaluation using SAR images shows that the new algorithm produces significantly high-quality segmentations as compared with state-of-the-art segmentation algorithms.

Citation Download Citation

Can Wang, Weimin Su, Hong Gu, and Dachen Gong "Semisupervised synthetic aperture radar image segmentation with multilayer superpixels," Journal of Applied Remote Sensing 9(1), 095098 (14 January 2015). https://doi.org/10.1117/1.JRS.9.095098

Published: 14 January 2015

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