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An Efficient Feature Selection for SAR Target Classification

机译：SAR目标分类的有效特征选择

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摘要

Selecting appropriate features is a prerequisite to attain the accuracy and the efficiency of SAR target classification. Inspired by the great success of BoVW, we address this issue by proposing an efficient feature selection method for SAR target classification. First. Graphic Histogram of oriented Gradients (HOG) based features is 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, only two bags of features are considered. Fourth, for best result and lower time complexity, the Discriminant Correlation Analysis (DCA) is used to combine relevant information forming new discriminant features. Finally, for target classification, SVM is used as a baseline classifier. Experiments on MSTAR public release dataset are conducted, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.
机译：选择合适的特征是获得SAR目标分类的准确性和效率的前提。受BoVW巨大成功的启发，我们通过提出一种用于SAR目标分类的有效特征选择方法来解决此问题。第一的。采用基于方向梯度（HOG）的图形直方图从训练SAR图像中提取特征。其次，使用K-means聚类算法生成判别码本。第三，在通过计算最接近的欧几里得距离进行特征编码后，仅考虑两个特征袋。第四，为了获得最佳结果并降低时间复杂度，使用判别相关分析（DCA）来组合相关信息，从而形成新的判别特征。最后，对于目标分类，将SVM用作基准分类器。对MSTAR公开发布数据集进行了实验，结果表明该方法优于最新方法。

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