提出了一种中高分辨力的航空航天遥感图像中飞机目标快速自动识别的新算法。在分割和分类过程中充分利用飞机目标的先验知识，提出了一种改进区域分割方法，并应用树分类器对飞机目标进行自动识别。所提出的改进区域分割方法较好地实现了区域分割中阈值的准确自动选取，克服了复杂背景图像中小目标的全局阈值自动分割的失效问题。采用二叉树分类器，通过提取简单的目标几何特征，分层进行种类识别，提高了识别速度，降低了漏检率和虚警率。运用该方法进行了实验。结果表明，识别率达到了100%。

A novel algorithm for automatic plane classification which is adapted to aeronautics and astronautics remote sensing images with mid-high resolution is proposed. Plenty of prior knowledge is used in the process of the segmentation and the classification. According to improved regional segmentation algorithm(IRSA),the threshold value is chosen accurately and automatically during the regional segmentation. The binary tree classifier is designed and applied to the plane automatic classification. Using simple geometric features which are extracted from the plane object,good effect and efficiency of classification are achieved. Several experiments to verify the proposed method are given with 100% detection rate,i. e. 0% false alarm rate and 0 % miss detection rate.