Abstract
In airplane target detection, there was the drawback of weak recognition ability for dark targets and high false alarm rate for detected targets. In order to address the problem, we proposed a detection method based on SAR and optical image feature fusion. It extracted texture, moment and backscattering characteristics from SAR images and combined with optical features. Moreover, the novel airplane edge templates incorporating SAR and optical images were created to acquire saliency map. During the process of detection, first, the saliency map and the One-Class-SVM (OCSVM) classifier were used to initially recognize the suspected airplane targets. Then, the combination features were adopted to further identify the misidentified airplane target. The experimental results showed that the Precision of the proposed method was 61.82% and the False Alarm Rate was 20%, which was better than the HIS-based detection method.
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