Bag-of-visual-words based feature extraction for SAR target classification

Feature extraction plays a key role in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very crucial to choose appropriate features to train a classifier, which is prerequisite. Inspired by the great success of Bag-of-Visual-Words (BoVW), we address the problem of feature extraction by proposing a novel feature extraction method for SAR target classification. First, Gabor based features are adopted to extract features from the training SAR images. Second, a discriminative codebook is generated using K-means clustering algorithm. Third, after feature encoding by computing the closest Euclidian distance, the targets are represented by new robust bag of features. Finally, for target classification, support vector machine (SVM) is used as a baseline classifier. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release dataset are conducted, and the classification accuracy and time complexity results demonstrate that the proposed method outperforms the state-of-the-art methods.