Automatic Registration of Remote Sensing Images Based on Revised SIFT With Trilateral Computation and Homogeneity Enforcement

Abstract

Automatic registration of remote sensing images is an essential task that requires the establishment of appropriate correspondences between the sensed image and the reference image. Among the feature-based matching approaches, attempts relied on the scale-invariant feature transform (SIFT) algorithm have shown particular superiority over other methods. From the perspective of automatic registration, the challenges of SIFT-based methods involve the elimination of mismatches and the homogeneity of matches. While many outlier removal methods have been proposed, the strategies for uniform distribution have been lacking. To address these two issues, this article investigates a new remote sensing image registration algorithm based upon a revised SIFT scheme. Additionally, an outlier removal mechanism founded on a trilateral computation (Tc) recipe and a homogeneity enforcement (He) layout according to a divide-and-conquer inclusion tactic are proposed. Finally, a stochastic competition process based upon game theory is introduced to secure an appropriate amount of correct matches in the proposed Tc and He (TcHe)-SIFT framework. A wide variety of multispectral and multitemporal remote sensing images with various scenarios were exploited to evaluate the proposed registration algorithm. Experimental results demonstrated the advantages of our developed remote sensing image registration algorithm over the state-of-the-art SIFT-based methods. We believe that this new registration technique is of potential in a number of remote sensing image processing applications.