Hierarchical Filtering Strategy for Registration of Remote Sensing Images of Coral Reefs

Registration of remote sensing images of coral reefs is basis for detection and analysis of changes to coral reefs, which is difficult due to inadequate and unstable texture information. Affine invariant features matching (AIFM) method, combining maximally stable extremal region (MSER) detector and scale invariant feature transformation (SIFT) descriptor, followed by optimizing using RANdom SAmple Consensus (RANSAC), still cannot lead to satisfactory results of image registration. To address this problem, we propose a hierarchical filtering strategy for image registration, which is composed of two stages. Filter 1, constructed based on a geometric transformation model determined by the corresponding contours of coral reefs, is employed to remove the wrong matching pairs which obviously dissatisfy spatial distributions of matching features. Based on the filtering results of Filters 1 and 2, with an appropriate threshold value determined by overlapping ratio of the corresponding coral reefs, can lead to further optimized results for image registration. This threshold is used to filter all erroneous matching pairs, while retaining as many correct matching pairs as possible. To test the effect of these filters, we design two experiments to compare the four approaches: AIFM without filtering, AIFM with Filter 1, AIFM with Filter 2, and AIFM with hierarchical filtering (Filter 1 + Filter 2). The experimental results demonstrate that the approach with hierarchical filtering performs much better than the other approaches.