Deep feature extraction and combination for synthetic aperture radar target classification

Moussa Amrani; Feng Jiang

doi:10.1117/1.JRS.11.042616

17 October 2017 Deep feature extraction and combination for synthetic aperture radar target classification

Moussa Amrani, Feng Jiang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 042616 (October 2017). https://doi.org/10.1117/1.JRS.11.042616

Abstract

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Citation Download Citation

Moussa Amrani and Feng Jiang "Deep feature extraction and combination for synthetic aperture radar target classification," Journal of Applied Remote Sensing 11(4), 042616 (17 October 2017). https://doi.org/10.1117/1.JRS.11.042616

Received: 8 June 2017; Accepted: 22 September 2017; Published: 17 October 2017

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