Target classification for ISAR image based on histogram statistics

Local Gabor binary patterns (LGBP) method in face recognition is improved and applied in inverse synthetic aperture radar (ISAR) target recognition. Firstly, the corresponding Gabor magnitude maps (GMMs) are obtained by convolving the enhanced ISAR image with multi-scale and multi-orientation Gabor filters. Then, each GMM is divided into small regions from which multi-scale block local binary pattern is used to extract histogram features. At last, five-type aircraft models are classified by using a nearest neighbor classifier with Chi square as a dissimilarity measure in the computed feature space. Compared with other recognition methods, the numerical results show that the proposed method is effective and has higher recognition performance.