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Journal of Applied Remote Sensing • Open Access

Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation
Author(s): Hang Yu; Luping Xu; Dongzhu Feng; Xiaochuan He

Paper Abstract

Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time.

Paper Details

Date Published: 11 September 2015
PDF: 21 pages
J. Appl. Remote Sens. 9(1) 095060 doi: 10.1117/1.JRS.9.095060
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Hang Yu, Xidian Univ. (China)
Luping Xu, Xidian Univ. (China)
Dongzhu Feng, Xidian Univ. (China)
Xiaochuan He, Xidian Univ. (China)


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