Target discrimination method for SAR images based on semisupervised co-training

Yan Wang; Lan Du; Hui Dai

doi:10.1117/1.JRS.12.015004

12 January 2018 Target discrimination method for SAR images based on semisupervised co-training

Yan Wang, Lan Du, Hui Dai

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 015004 (January 2018). https://doi.org/10.1117/1.JRS.12.015004

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.

Citation Download Citation

Yan Wang, Lan Du, and Hui Dai "Target discrimination method for SAR images based on semisupervised co-training," Journal of Applied Remote Sensing 12(1), 015004 (12 January 2018). https://doi.org/10.1117/1.JRS.12.015004

Received: 26 May 2017; Accepted: 12 December 2017; Published: 12 January 2018

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