Modified SIFT for multi-modal remote sensing image registration

The scale invariant feature transform (SIFT) is a widely used method for image registration and object recognition. The SIFT method is well known for its ability to identify objects at varying scales and rotations among clutter and occlusion with very fast processing time. The application of SIFT on multi-modal remote sensing images for image registration purposes, however, often results in inaccurate and sometimes incorrect matching. Commonly a very large number of feature points are generated from a remote sensing image but a very small number of feature points are matched giving a high false alarm rate. This paper proposes a method containing several modifications to improve the feature matching performance of the SIFT algorithm by adapting it to suit the characteristics of remote sensing images. The proposed method leads to more matching points with a significantly higher rate of correct matches.