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2017 | OriginalPaper | Buchkapitel

An Improved Feature Selection Method for Target Discrimination in SAR Images

verfasst von : Yanyan Li, Aihua Cai

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

Due to synthetic aperture radar (SAR) imaging principals, at particular azimuth or depression angle, targets and clutters may become very hard to distinguish, to solve this problem, many complicated features have been developed, this is not only a tough work, but little improvement of discrimination accuracy is obtained. In this paper, an improved target discrimination method is proposed, one-class quadratic discriminator (OCQD) has been used, compared with traditional method using Bayes discriminator, when number of features is limited, our new method has higher target classification correction than old methods, considering that target classification correction is more important than clutter classification correction, our proposed method has a good performance on target discrimination. First, discrimination scheme based on genetic algorithm (GA) is introduced. Second, feature extraction algorithms of SAR images have been introduced. Third, an improved feature selection method based on GA has been proposed, in which the OCQD has been used and a new fitness function has been designed. Finally, the theory of OCQD algorithm is explained. According to the experiment result based on moving and stationary target acquirement and recognition (MSTAR) database, our new method reduces target undetected rate by 1.5% compared to the state-of-the-art methods in target discrimination, besides, the efficiency of feature selection based on GA has been improved by 77%.

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Metadaten
Titel
An Improved Feature Selection Method for Target Discrimination in SAR Images
verfasst von
Yanyan Li
Aihua Cai
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-71598-8_56