为了在机载光子雷达条件下实现远距离舰船类型精确识别的目标，首先对场景点云进行平面拟合、点云聚类、目标提取等处理，获得舰船点云；然后对船体点云提取体量、表面法向量直方图、甲板目标分布等三维特征，获得特征数组；最后利用随机森林对抽取的特征进行判别分类，实现船体类型精确识别。实验表明：通过对13种类型船只的多次分类实验，平均正确识别率在95%以上，有效实现了舰船的类型识别。

In order to accurately identify the target of the long-distance ship type under the condition of airborne photon radar, the perform plane fitting, point cloud clustering, target extraction and other processing on the scene point cloud to obtain the ship point cloud firstly. Then from the hull point cloud, three-dimensional features such as volume, surface normal vector histogram, and deck target distribution are extracted to obtain a feature array. Finally, the random forest is used to discriminate and classify the extracted features to realize the accurate identification of the hull type. Experiments show that through multiple classification experiments on 13 types of ships, the average correct recognition rate is above 95%, effectively realizing ship type recognition