For the problem of the small target and the weak contrast of UAV image, we propose a method for minimal target detection based on simple linear iterative clustering (SLIC) hierarchical segmentation. Firstly, pretreatment methods are utilized to improve the contrast of the original image, and Top-hat fusion is used as initial segmentation to detect the initial target area. Then SLIC s egmentation method is utilized to obtain the fine segmentation, and improved density-based spatial clustering of applications with noise(DBSCAN) is introduced to accomplish ultra-pixel classification according to the segmentation result. Finally, the target is detected through feature matching by extracting the neighborhood entropy of the target and other low-level features. Also a detection strategy combining global detection and local detection is proposed to accelerate the detection speed. The experimental results show that the proposed method can improve the detection performance for the minimal targets in UAV image and accelerate the detection speed.