In order to recognize targets in images fast and truly, an automatic target recognition method was proposed based on image entropy and speed up robust feature. First, image entropy was computed in different blocks, and regions full of texture were filtered out by threshold. The local key points in regions of interest were extracted by incorporating the Hessian and Harris detectors. Then, feature descriptors were established and principle component analysis was employed to reduce the dimensionality. Finally, nearest neighbor distance ratio classifier was explored in double directions and wrong matches were eliminated by random sample consensus. The experiment results demonstrate that the recognition rates for images in simulation database with varied view-points, scales and illuminations are 87.12%, 75.31% and 84.98%, and the computing time is 70.35 ms, 71.27 ms and 220.63 ms, respectively. Moreover, the correct matching rate for an aerial large planar array image of 8 956×6 708 pixels is 78.13% and the computing time is 68.09 s. Compared with speed up robust feature, the proposed method performs better both in recognition rates and computing time.