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Proceedings Paper

SAR image segmentation using skeleton-based fuzzy clustering
Author(s): Yun Yi Cao; Yan Qiu Chen
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Paper Abstract

SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.

Paper Details

Date Published: 11 June 2003
PDF: 9 pages
Proc. SPIE 4898, Image Processing and Pattern Recognition in Remote Sensing, (11 June 2003); doi: 10.1117/12.467811
Show Author Affiliations
Yun Yi Cao, Fudan Univ. (China)
Yan Qiu Chen, Fudan Univ. (China)


Published in SPIE Proceedings Vol. 4898:
Image Processing and Pattern Recognition in Remote Sensing
Stephen G. Ungar; Shiyi Mao; Yoshifumi Yasuoka, Editor(s)

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