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Journal of Applied Remote Sensing

Target discrimination method for SAR images based on semisupervised co-training
Author(s): Yan Wang; Lan Du; Hui Dai
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Paper Abstract

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

Paper Details

Date Published: 12 January 2018
PDF: 11 pages
J. Appl. Rem. Sens. 12(1) 015004 doi: 10.1117/1.JRS.12.015004
Published in: Journal of Applied Remote Sensing Volume 12, Issue 1
Show Author Affiliations
Yan Wang, Xidian Univ. (China)
Lan Du, Xidian Univ. (China)
Hui Dai, Xidian Univ. (China)

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