Outlier removal and feature point pairs optimization for piecewise linear transformation in the co-registration of very high-resolution optical remote sensing imagery

Abstract

Co-registration of very high-resolution images is a challenging and essential preprocessing step in remote sensing image analysis. Piecewise linear (PL) transformation has been shown to accurately handle the registration of very high-resolution images with various local deformations. However, the PL transformation has strict requirements on the structure of the local triangulated irregular network (TIN) and the position accuracy of feature point pairs (FPPs). Existing outlier removal methods barely consider the local TIN structure of FPPs, thus failing to meet the requirements of the PL transformation. Moreover, the inevitably remaining outliers lead to lower local registration accuracy. This paper proposes an outlier removal method and an FPPs optimization method to improve the registration accuracy of the PL transformation. First, we developed a triangle-area representation ratio of the triangulated irregular network (TIN-TARR) descriptor to identify the outliers. Then, we divided these outliers into three types and removed them iteratively. Finally, we further optimized the FPPs by maximizing the similarity between the fixed and the registered images to improve the registration accuracy of the PL transformation. The proposed method was validated using three pairs of TripleSat-2 images with a spatial resolution of 3.2 m, one pair of WorldView-2 images with a spatial resolution of 1.6 m, and one pair of aerial images with a spatial resolution of 0.3 m. The results demonstrated that: (1) The obvious deformations in each registered image were eliminated after the outlier removal using TIN-TARR. (2) The FPPs optimization significantly improved the registration accuracy of the PL transformation. The average registration errors in five tests were 0.542, 0.389, 0.456, 0.420, and 0.592 pixels, respectively. (3) Different similarity metrics had various effects on the FPPs optimization, and structural similarity was the most effective measurement. (4) The proposed co-registration framework presented better registration accuracy than three state-of-the-art methods, especially in images with land cover changes and shadows.