A Robust Seamline Extraction Method for Large-Scale Orthoimages Using an Adaptive Cost A* Algorithm

During the mosaicking of orthophotos, geometric and radiometric inconsistencies between adjacent images can cause misalignments at the boundaries, necessitating seamline detection to bypass prominent ground features. Existing methods struggle to simultaneously circumvent various ground features such as buildings, ridges, and farmland in large-scale remote sensing images with rich ground features.This leads to insufficient robustness in densely built urban areas and mountainous village regions. To address this issue, this paper proposes a seamline extraction method based on an adaptive cost A* algorithm. Initially, the seamline network is extracted as a distance-cost constraint to limit the search range of the algorithm, ensuring efficiency. Then, an improved A* algorithm is employed to optimize the seamline, utilizing an adaptive obstacle threshold and Structural Similarity Index (SSIM) to construct the heuristic function. This approach effectively bypasses prominent ground features in both urban and mountainous areas. The method does not require pre-extraction of image features and constructing a cost map; instead, it dynamically detects impassable regions in the overlapping areas during the A* path search process. Finally, the image is reprojected and mosaicked based on the seamline network. This method does not rely on auxiliary data and is more suitable for large-scale image datasets with extensive coverage. Experimental results demonstrate that, compared to existing methods and commercial software, the proposed method shows superior radiometric and geometric consistency, overcoming the robustness issues of current large-scale remote sensing image mosaicking methods.