基于直方图统计量的逆合成孔径雷达目标识别

将原用于人脸识别的基于Gabor局部二进制模式的识别技术用于逆合成孔径雷达(ISAR)像的雷达目标识别,对算法进行了改进,取得了较好的识别效果。将ISAR像进行Gabor小波变换,提取不同尺度和方向的Gabor幅值图谱;然后把幅值图谱分成小的子区域,用多尺度局部二值模式提取空域增强的直方图作为特征,最后在χ2统计量作为不相似度量计算的特征空间里,采用最近邻分类器完成五类目标的分类识别。与目前已有的几种典型ISAR目标识别方法进行了对比,结果表明:该方法是可行且有效的,能够明显地提高识别率。

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.