Improved SAR target recognition by selecting moment methods based on Fisher score

SAR generates high-resolution images irrespective of any weather condition and solar illumination. Feature-level fusion increases the dimensionally of feature space as well as feature redundancy brought by correlation among the features. In this paper, we propose a technique to select the most discriminative feature extraction techniques based on Fisher score. In this regard, by utilizing different moment methods, we extract moment features and evaluate Fisher scores of particular moment method followed by moment method ranking. Finally, we select top moment methods for feature fusion. The proposed technique improves accuracy while decreasing the feature dimensionality and the feature redundancy. The performance of the proposed method improves individual performances of the moment methods considered. Furthermore, results support the superiority of this proposed moment-based technique over the state-of-the-art methods in the literature.