相干激光雷达距离像与目标表面物理结构特性密切相关，体现目标的本质特征，在目标识别领域引起广泛关注。数据采集过程和采集成本决定了激光雷达不容易采集到大量的图像。在小样本情况下，随着特征维数的增加，识别率可能会下降，即出现休斯现象。为此，把两种特征选择算法——Relief算法和支持向量机回归特征消去(SVM-RFE)算法引入到距离像目标识别。仿真实验结果表明，在3个训练样本时，利用Relief 和SVM-RFE算法，可以解决由三组组合矩（Hu矩和仿射矩，仿射矩和Zernike矩以及仿射矩、Hu矩和Zernike矩）引起的休斯现象，并且基于SVM-RFE算法的识别性能略好于Relief算法的识别性能。

Coherent ladar range image is closely relative to physical structure property of object′s surface, and reflects essential characteristics of objects, thus it has received considerable attention in object recognition fields. Generally, it is difficult to collect a mass of range images for ladar in real application. However, under small-sample case, when the number of features increases, the recognition rate will decrease, which is called Hughes effect. In this paper, the two methods of feature selection, i. e., relief and support vector machine recursive feature elimination (SVM-RFE) are applied to solve the problem. The experimental results demonstrate that the methods of Relief and SVM-RFE are able to relieve Hughes effect that is caused by three combined moments [affine moments (AMs) and Zernike moments (ZMs), ZMs and Hu moments (HMs), and AMs, HMs and ZMs]. Moreover, the recognition rate of SVM-RFE is slightly better than that of Relief.