提取高山林地区域无人机影像上的角点特征时，运用传统方法提取的角点特征数量偏少，为此提出了一种自适应的提取方法。该方法将角点特征提取过程系统性地分为粗提取和精提取过程，其中粗提取为利用吕言算子从影像上确定候选角点特征，而精提取则是通过改进后的SUSAN算法从候选角点特征中精确提取角点特征。角点特征提取结果验证了该方法的可行性，与传统方法相比，该方法提取的角点特征数量更多，运行效率更快，适用于高山林地区域无人机影像角点特征提取。

During the corner feature extraction of unmanned aerial vehicle (UAV) images of high mountains and dense forests, the number of corner features was small by using traditional methods, thus a self-adaption extraction method being suggested, in which the whole process could be divided into two parts. The first part is primary corner feature extraction which determines the candidate corner feature from images with the Lv-Yan operator. While the second part is accurate corner feature extraction, which accurately extractes corner feature from the candidate corner feature with improved SUSAN algorithm. The experimental results have proved the feasibility of the proposed approach, which could extract greater number of corner feature points and run faster compared with traditional methods, as well as adapt to the corner feature extraction of UAV images in high mountains and dense forests.