In order to obtain image features that can be well exploited in long distance target recognition and are computationally efficient,an algorithm combining scale-space FAST(Features from Accelerated Segment Test)corner detector and SURF(Speeded Up Robust Features)descriptor,is proposed in this paper.The Fast-Hessian matrix based keypoint detector used SURF algorithm,is apt to extract numerous keypoints from non-informative edges with relatively high Fast-Hessian response,leading to considerable amounts of low-distinctive feature and consequently high rates of mismatch;with Gaussian filters employed,the possible amount of keypoints extracted with Fast-Hessian from regions of small targets is largely reduced due to image blur,which leaves difficulty for recognition of long distance targets.To address these problems,the proposed method uses a scale-space FAST corner detector in place of the Fast-Hessian detector,combining with SURF descriptor for its distinctiveness.The proposed method effectively eliminates the problem of extracting interfering keypoints along image edges,performing a significantly better detection of keypoints on long distance targets than Fast-Hessian,and generates features of better distinctiveness than BRISK features,which uses FAST as well.The experimental results indicate that the recognition algorithm based on the proposed features gives better performance against targets with change in scale,illumination and 3Dviewpoint than that either based on SIFT,SURF or BRISK;the proposed feature can be well applied to long distance target recognition,while reaching a comparable computation speed to SURF.