Enhanced Incremental Image Stitching for Low-Altitude UAV Imagery With Depth Estimation

This letter proposes Depth-Aided Incremental Image Stitching (DAIIS), an algorithm tailored for low-altitude unmanned aerial vehicle (UAV) images in urban environments. DAIIS integrates single-view depth estimation and plane fitting to identify ground feature points, enabling precise computation of transformation matrices and significantly reducing stitching errors caused by large depth variations. Unlike traditional methods, DAIIS effectively addresses misalignments and artifacts, producing seamless and accurate stitched images that closely resemble the real scene. Evaluated on three distinct datasets, DAIIS demonstrated superior performance, achieving a 70% reduction in mean square error (mse) and notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to other methods. The results validate DAIIS as a robust solution for high-precision image stitching, enhancing accuracy and visual quality in challenging low-altitude scenarios, which offers reliable and high-quality results for various urban applications.