LargeStitch: Efficient Seamless Stitching of Large-Size Aerial Images via Deep Matching and Seam-Band Fusion

High-resolution panoramas generated by UAV image stitching are indispensable image resources for remote sensing applications. However, most existing stitching methods are designed for small-size images, making it difficult to process large-size images efficiently, leading to problems such as image feature misalignment and low generation efficiency. This paper presents LargeStitch, a novel batch stitching method for large-size UAV images. The method introduces advanced image matching and alignment strategies through deep learning techniques to achieve efficient extraction and accurate alignment of dense features. To further optimize the stitching effect, this paper also proposes a seamless fusion method based on Seam-band, which effectively solves the problem of ghosting and misalignment in the overlapping region of large-size images. In addition, we designed a mask-based pre-stitching image filtering strategy, which optimizes the selection of candidate images to reduce content redundancy, thereby effectively avoiding unnecessary computational overhead and time consumption. The experimental results show that LargeStitch is not only capable of realizing fast stitching of high-precision and large-size aerial images but also significantly outperforms existing methods in terms of stitching quality and processing efficiency, making it a practical solution for realizing high-efficiency and seamless aerial image stitching.