Abstract：Scale invariant feature transform(SIFT) is a popular feature extraction algorithm that has applied to remote sensing image automatic registration; nevertheless, there exists a problem in the remote sensing image automatic registration based on SIFT algorithm, i.e., the distribution of feature points is always nonuniform. An automatic image registration algorithm based on potential function model is presented in this paper, which can solve the problem of optimizing nonuniformity in feature point distribution in SIFT. By adjusting the threshold of SIFT, the number of matching points is promoted. The algorithm can optimize the uniformity in feature point distribution by potential model function in molecular mechanics, and make the low-precision feature point to the sparse area of feature points. Then it revises local mutual information to improve matching point accuracy, so as to realize a high quality (uniform space distribution, high accuracy of Sub-Pixel registration) automatic image registration finally.
孙彬, 边辉, 王培忠. 基于势函数点分布调整的SIFT图像配准算法[J]. 国土资源遥感, 2015, 27(3): 36-41.
SUN Bin, BIAN Hui, WANG Peizhong. Image registration algorithm based on SIFT and potential function adjusting location of points. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 36-41.
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2015, Vol. 27 
