SAR image segmentation using skeleton-based fuzzy clustering

Yun Yi Cao; Yan Qiu Chen

doi:10.1117/12.467811

11 June 2003 SAR image segmentation using skeleton-based fuzzy clustering

Yun Yi Cao, Yan Qiu Chen

Proceedings Volume 4898, Image Processing and Pattern Recognition in Remote Sensing; (2003) https://doi.org/10.1117/12.467811
Event: Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2002, Hangzhou, China

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.

Citation Download Citation

Yun Yi Cao and Yan Qiu Chen "SAR image segmentation using skeleton-based fuzzy clustering", Proc. SPIE 4898, Image Processing and Pattern Recognition in Remote Sensing, (11 June 2003); https://doi.org/10.1117/12.467811

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