The matched filter (MF)algorithm can judge the target and background by comparing the Marmanian distance between the pixels to be detected and the center of the target class and the background class,but it cannot know the relevant statistical information of the background and target of hyperspectral image,so it can only make some assumptions,leading to inaccurate detection results.In this paper,after MF detection is completed,threshold is set for segmentation,background and target are reclassified,and relevant statistical information of background and target is obtained.In order to better obtain the two types of statistics,we use the iterative method to obtain the two types of statistics level by level,and finally use the MF algorithm to carry out target detection.In order to enlarge the difference between the target and the background and increase the separability of the target and the background,zero vector is added to the pixel to adjust the pixel value.Two groups of real hyperspectral data were used to conduct target detection experiments.By comparing the value of ROC curve and AUC of the area under the curve,it was found that the algorithm proposed in this paper could improve the accuracy of hyperspectral target detection compared with MF algorithm and some traditional target detection algorithms.