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Erschienen in:
2017 | OriginalPaper | Buchkapitel
SAR Automatic Target Recognition Based on Deep Convolutional Neural Network
verfasst von : Ying Xu, Kaipin Liu, Zilu Ying, Lijuan Shang, Jian Liu, Yikui Zhai, Vincenzo Piuri, Fabio Scotti
Erschienen in: Image and Graphics
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Abstract
In the past years, researchers have shown more and more interests in synthetic aperture radar (SAR) automatic target recognition (ATR), and many methods have been proposed and studied for radar target recognition. Recently, deep learning methods, especially deep convolutional neural networks (CNN) has proven extremely competitive in image and speech recognition tasks. In this paper, a deep CNN model has been proposed for SAR automatic target recognition. The proposed deep model named SARnet, has two stage convolutional-pooling layers and two full-connected layers. Due to the demand of requirement of large scale of the data in deep learning, we proposed an augmentation method to get a large scale database for the training of CNN model, by which the CNN model can learn more useful features through the large scale database. Experimental results on the MSTAR database show the effectiveness of the proposed model and has achieved encouraging results with a correct recognition rate of 95.68%.