Abstract: Aiming at the problem that the number of homonymous feature points is insufficient or even mismatched due to the large-scale difference between images in image matching， this paper proposes a heterologous image SIFT matching method （BS-SIFT） using word bag model to estimate the scale difference. By sensing the scale difference between the images to be matched in advance， this method transforms the heterogenous image matching into starting at the same scale， improves the interior point rate of matching， and then increases the number of matching points of large-scale difference images. Firstly， by aggregating continuously changing image feature points of different scales in the feature space and reallocating image features of each scale to feature center， the feature distribution relationship of each scale is obtained. Secondly， the scale descriptor between images to be matched is obtained by combining the spatial information entropic weighting of image feature center. Finally， the best image scale difference can be obtained by analyzing the distance distribution of scale descriptors. The experimental results show that the BS-SIFT algorithm proposed in this paper can still achieve good results in image matching with a scale difference of more than 10 times. Compared with the classical SIFT algorithm， the algorithm proposed in this paper can significantly obtain more homonymous feature points while achieving higher efficiency， and the matching accuracy is improved by at least 9 percentage points and up to 37 percentage points.

Key words: image matching, SIFT, word bag model, large scale difference, feature description, aerospace image