Abstract: Duo to including the ground control points that choosed by manual geometric precision correction were not precise, and the existing methods ignorded the spectrum consistency of hyperspectral data, an automatic geometric precision correction method based on SIFT feature was proposed to solve the problems. SIFT feature was extracted from the image and the geographic coordinate of the hyperspectral data was used to accomplish local feature matching. In order to extract high-precision and uniformly distributed ground control points, a sub-regional Random Sample Consensus(RANSAC) algorithm was proposed. The airborne hyperspectral data collected by HyMap in Dongtianshang, Xinjiang Autonomous Region, was used to analyze and validate the performance of the algorithm. The CE90/CE95 and root mean square error were calculated to evaluate the geopositional accuracy. The results show that the automatic geometric correction method based on SIFT feature can achieve 0.8 pixel geopositional accuracy, and the spectrum of the spectrum angle between warp image and corrected image is less than 0.01 radian.