The ultimate goal of the development of aerial photogrammery is to acquire rapidly and accurately the ground measurements. However, traditional photogrammetric technologies, particularly in the continuous digital images stitching technique, is still very limited. In the past, the ground control points were used as the references for the image registration, however, it is very time and resource consuming, as well as human visual capability constraint. Accuracy and efficiency are two key factors which need to be enhanced to meet the practical requirement for aerial imageries stitching. The SIFT (Sale Invariant Feature Transform) algorithm was used in the computer vision to perform feature extraction in good condition. The extracted SIFT features are invariant to image scale, rotation, noise and change in illumination, and it is a robust and abundant feature extraction algorithm. SIFT algorithm extracts feature points from multi-scale space. For a large scale aerial image containing huge amount of image contents, it will spend a lot of time to extract features from imagery. Therefore, this study proposes a new method, called Inter-Grid Down-Sampling (IGDS) method, to reduce the image size and relative amount of image information to improve the computing efficiency. The correspondent extracted features are matched in the adjacent images with additional RANSAC outlier removal procedure to select correct and characteristic feature points. Finally the Hugin-Panorama Photo Stitching software is used to stitch all the continuous photogrammetric images for producing a panorama imagery of all flight lines. The experiment results indicate that sub-pixel accuracy for extracted feature points can be obtained when the down-sampling factor 3 was selected for the IGDS method, and it only needs half of the computing time. Compared to the Nearest-Neighbor Interpolation and Cubic Interpolation methods to reduce the image size, the IGDS method can increase more feature extraction efficiency without scarifying the location accuracy. When threshold value for SIFT was set between 0.4 to 0.6, we can achieve the largest correct matching rate. In addition, the RANSAC outlier removal procedure can effectively select the best matching feature points both in numbers and locations. For image stitching, the Hugin-panorama photo stitching software can effectively be used to match feature points and do geometric correction and color adjustment to obtain a consistent panorama imagery. Finally, the proposed method in this study can derive a low-variant in resolution and measurements significance for a stitching image from continuous aerial images.