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Content Provider | IEEE Xplore Digital Library |
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Author | Yunfei Shi Qian Song Tian Jin Zhimin Zhou |
Copyright Year | 2011 |
Description | Author affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China (Yunfei Shi; Qian Song; Tian Jin; Zhimin Zhou) |
Abstract | In order to solve the problem of landmine detection in Forward-Looking Ground Penetrating Virtual Aperture Radar (FLGPVAR), the AdaBoost classification with adaptive feature selection (AFS-AdaBoost) is proposed. The feature selection is added into the traditional AdaBoost, which can reduce the training error of weak classifiers and improve the generalization capability of a strong classifier. The feature selection is based on a wrapper model, whose cost function is the performance of the classifier. Considering landmine detection one-class classification problem, the false alarm rate with constant probability of detection is chosen to be the cost function, which ensures the detection performance of strong a classifier. Processing of a real dataset show that AFS-AdaBoost is applicable to the landmine detection in FLGPVAR. Compared with traditional AdaBoost, the detection performance and generalization capability of AFS-AdaBoost are significantly improved. |
Starting Page | 1 |
Ending Page | 5 |
File Size | 392651 |
Page Count | 5 |
File Format | |
ISBN | 9781457703324 |
DOI | 10.1109/IWAGPR.2011.5963887 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-06-22 |
Publisher Place | Germany |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Landmine detection Feature extraction Training Clutter Mathematical model Cost function Radar imaging AdaBoost Forward-Looking Ground Penetrating Virtual Aperture Radar Landmine Detection Feature Selection |
Content Type | Text |
Resource Type | Article |
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