Experiences with Using Sift for Multiple Image Domain Matching

The paper reports about investigations into the utilization of the SIFT algorithm to support image matching between different image domains. The Scale-Invariant Feature Transformation, proposed by Lowe in 1999, is a highly robust technique that has been widely used in the computer vision community. Though, SIFT is known in mapping circles, so far its use is rather limited. The objective of our study is to assess the performance of SIFT when it is applied to imagery acquired by different sensors and on different platforms. For testing, four image datasets from different sensors were considered, including airborne and satellite imagery, and LiDAR intensity and elevation. The image co-registration was performed based on SIFT features. The preliminary results indicate mixed but encouraging performance, as the in-domain matching based registration generally works well, while the matching between images coming from different domains (such as airborne and LiDAR intensity) usually produces modest results. In all cases, we used RANSAC to remove outliers. The above behavior could be associated with two facts: first, the style of SIFT features extracted from the imagery is correlated to the image type, and second, the number of actually matched features is significantly lower than that of the in-domain imagery. In summary, our present level of research indicates that the SIFT algorithm has substantial capacity to support image matching between different domains, and thus appears to be an efficient technique for co-registering image data acquired by different sensors.