Efficient UAV High-Resolution Image Stitching via Dense Deep Kernelized Feature

Unmanned aerial vehicle (UAV) image stitching aims to generate panoramic remote sensing images beyond the field of view of a single camera. However, there are still significant challenges in constructing panoramic images of a target area quickly and accurately, especially in terms of computationally intensive feature matching extraction and feature alignment accuracy, which are particularly sensitive to high-resolution and low-texture scenes. To address this problem, this study proposes an efficient image stitching method that incorporates dense depth kernelized feature extraction and geometric constraint optimization. The learning-based kernelized feature matching framework is adopted to achieve subpixel-level dense matching, which effectively overcomes the time-consuming and sparse matching deficiencies of traditional manual features (e.g., SIFT) in high-resolution images. Second, a two-layer geometrically constrained mismatching filtering strategy is designed, which significantly improves the alignment accuracy in low-texture and large-parallax scenarios. Finally, panoramic stitching is achieved through a hybrid strategy consisting of a single-responsive transform and max-intensity pixel blending strategy to realize panoramic stitching. Experimental results obtained on multiple datasets show that the proposed method achieves similar visual quality metrics (PSNR, SSIM, and LPIPS) while reducing the stitching time to just 17.5% of that of the baseline method. This makes it a practical solution for efficiently stitching large UAV images.