中文摘要：

针对传统的光谱角匹配分类算法仅考虑光谱信息,导致混合像元易出现错分和分类结果中出现＂麻点＂等问题,该文考虑地物连续性这一特点,提出了一种结合像元空间邻域信息对光谱角进行修正的光谱角匹配分类算法。该方法不仅保留了传统光谱角度匹配算法不受增益因素影响和减弱地形对照度影响等优点,并且减小了混合像元被错分的概率。基于ROSIS获取的Pavia大学校园的高光谱影像分类结果表明：加入像元空间邻域信息后的光谱角匹配算法在保证分类精度的同时,有效地减弱了分类结果中的＂麻点＂现象,验证了该文方法的可行性、有效性。

英文摘要：

Based on laser radar range equation, the model about geometric accuracy of point cloud under different angles of incidence, range and reflectivity was established. Plane fitting accuracy was made as the evaluate index of point cloud＇ s geometric accuracy. Faro Focus 3D was used under different angles of incidence and range of 6 different materials plane. The results showed that the vertical direction in the magnitude of the error correction was reduced and the model was effective after adding reflectance effect model.