REMOTE SENSING IMAGE MATCHING USING TPS TRANSFORMATION AND LOCAL GEOMETRICAL CONSTRAINT

Focusing on the characteristics of remote sensing images, this study proposes a new algorithm for feature matching of remote sensing images to eliminate mismatch. The algorithm utilizes feature descriptors, such as scale-invariant feature transform, for rough correspondence and the thin-plate spline for non-rigid transformation. Under the Bayesian framework, correspondence and transformation are alternately optimized by the expectation-maximization algorithm to automatically eliminate mismatched points. We also introduce a local geometrical constraint to maintain the internal structure of adjacent feature points. We apply this method to a large number of remote sensing images, and the experimental results reveal the method's superiority over the state-of-the-art.