Optimizing Local Alignment along the Seamline for Parallax-Tolerant Orthoimage Mosaicking

Abstract

Orthoimage mosaicking with obvious parallax caused by geometric misalignment is a challenging problem in the field of remote sensing. Because the obvious objects are not included in the digital terrain model (DTM), large parallax exists in these objects. A common strategy is to search an optimal seamline between orthoimages, avoiding the majority of obvious objects. However, stitching artifacts may remain because (1) the seamline may still cross several obvious objects and (2) the orthoimages may not be precisely aligned in geometry when the accuracy of the DTM is low. While applying general image warping methods to orthoimages can improve the local geometric consistency of adjacent images, these methods usually significantly modify the geometric properties of orthophoto maps. To the best of our knowledge, no approach has been proposed in the field of remote sensing to solve the problem of local geometric misalignments after orthoimage mosaicking with obvious parallax. In this paper, we creatively propose a method to optimize local alignment along the seamline after seamline detection. It consists of the following main processes. First, we locate regions with geometric misalignments along the seamline based on the similarity measure. Second, for any one region, we find one-dimensional (1D) feature matches along the seamline using a semi-global matching approach. The deformation vectors are calculated for these matches. Third, these deformation vectors are robustly and smoothly propagated into the buffer region centered on the seamline by minimizing the associated energy function. Finally, we directly warp the orthoimages to eliminate the local parallax under the guidance of dense deformation vectors. The experimental results on several groups of orthoimages show that our proposed approach is capable of eliminating the local parallax existing in the seamline while preserving most geometric properties of digital orthophoto maps, and that it outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

An interpretable approach for automatic aesthetic assessment of remote sensing images

“…Finally, images should not contain any artifacts. Artifacts can appear during image mosaicking as a result of color differences or geometric misalignments between adjacent images ( Yin et al, 2022 ), as shown in Figure 2C .…”

An Upscaling–Downscaling Optimal Seamline Detection Algorithm for Very Large Remote Sensing Image Mosaicking

“…Various algorithms have been developed for such image mosaicking. These existing algorithms can be broadly classified into two categories: To find the minimum color difference between the two sides of the seamline [2][3][4][5][6] and to find the minimum number of features to be crossed by the mosaicking procedure [7][8][9][10]. The former exploits image color information to guide the seamline, avoiding seamlines through areas with large color contrasts and ensuring that color differences along the seamline are minimized.…”