提出了一种基于扩展数学形态学和光谱角度匹配相结合的高光谱亚像元目标检测算法。在目标与背景未知的情况下，同时利用光谱和空间信息实现目标的定位与检测，实现高光谱亚像元目标的检测识别。通过扩展的形态学膨胀和腐蚀运算实现端元提取，采用光谱角度匹配算法进行感兴趣目标的检测识别。算法性能通过AVIRIS数据进行评价，与仅利用光谱角度匹配的算法和RX异常检测算法进行比较。实验证明，所提出的算法性能优于其他两种算法，具有低虚警率的亚像元目标检测结果。

A hyperspectral subpixel target detection algorithm was proposed based on extended mathematical morphology and spectral angle mapping. The spectral and spatial information had been used to locate and detect targets under the condition that prior knowledge of targets and background was unknown. Then hyperspectral subpixel targets was detected and recognized. The extended mathematical morphological erosion and dilation operations were performed respectively to extract endmembers. The spectral angle mapping method was used to detect and recognize interested targets. The hyperspectral image collected by AVIRIS was applied to evaluate the proposed algorithm. The proposed algorithm was compared with SAM algorithm and RX algorithm by a specifically designed experiment. From the results of the experiments, it is illuminated that the proposed algorithm can detect subpixel targets with low false alarm rate and its performance is better than that of the other algorithms under the same condition.