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.

The global optimization-based color correction approach aims to minimize the color differences of multiple images by optimizing the correction model for each image. The color differences in multisource and multitemporal remote sensing images are difficult to express using a simple correction model with few parameters. When employing a more flexible correction model, the number of correction parameters and optimization equations grows rapidly with the increase in the number and resolution of input images. In addition, the correction parameters of all images are coupled together and need to be solved simultaneously. An excessive number of parameters results in solving slowly or potential failure. To solve this problem, we propose a parallelizable color correction approach that decouples the correlation of correction parameters in the optimization equations and optimizes each image separately. First, we introduce auxiliary variables that replace values related to other images in the cost function. Second, we construct optimization equations for each image and parallelly solve the correction parameters. Finally, we correct the input images through a weighted correction model to better eliminate correction artifacts. Our approach iteratively optimizes auxiliary variables and correction parameters until the correction results converge. The experimental results on several challenging datasets show that our approach significantly improves execution efficiency and obtains the global optimal solution using the flexible correction model.

Orthoimage mosaicking aims to make the seamline bypass obvious objects to produce seamless digital orthophoto maps. Nevertheless, stitching artifacts may still occur when obvious objects are inevitably crossed by the seamline or when the orthoimages are not accurately aligned in geometry. Image warping is necessary to correct the geometric misalignments. Existing image warping approaches primarily focus on natural images, with relatively few studies targeting orthoimages. Unlike natural images, orthoimages have significant geometric attributes and no longer satisfy the epipolar constraint. Therefore, most existing image warping methods are ineffective in dealing with geometric misalignment in orthoimage mosaicking. To solve the above problems, we propose a coarse-to-fine image warping approach using trust region optimization for orthoimage mosaicking. Firstly, we obtain an optimal seamline and search for geometrically misaligned regions along the seamline. Secondly, for each region, we model the geometric alignment problem as an optimization problem of the deformation vectors for pixels. Next, the trust region method is employed to iteratively solve the optimization problem. We introduced the image pyramid strategy to achieve efficient coarse-to-fine optimization and help to avoid local optima. Finally, the orthoimages are warped based on the optimized pixel deformation vectors. However, when dealing with misaligned regions covered by multiple images, the frame-to-frame warping cannot achieve the optimal result. Therefore, we propose a multi-image joint optimization strategy that introduces auxiliary variables to fuse information from multiple orthoimages, ensuring seamless mosaicking. Experimental results demonstrate that whether in two-orthoimage or multi-orthoimage mosaicking, our solution has better visual and quantitative performance.