Distributed multi-GPU algorithm for accurate registration of UAV-based multispectral and multitemporal orthomosaics

Accurate registration of high-resolution multispectral UAV orthomosaics acquired on different dates and sensors is essential for a wide range of remote sensing applications. However, this task remains challenging due to variations in acquisition conditions, including seasonal changes, differences in illumination, weather, and sensor characteristics. This article presents a parallel multilevel registration method that combines and improves two existing algorithms: HSI-KAZE, a feature-based approach, and HYFM, an area-based method. The three-level approach first applies an optimized HSI-KAZE (OHSI-KAZE) for coarse estimation of scale, rotation, and translation, followed by HYFM for fine correction. The multi-node multi-GPU proposed implementation, leveraging MPI, OpenMP, and CUDA, enables efficient processing on GPU-accelerated HPC clusters. Experiments on six real multispectral orthomosaic pairs from river environments, compared against state-of-the-art classical and deep learning methods, achieve high registration accuracy with RMSE values below 1.57 pixels and a 30x speedup, confirming both the accuracy and scalability of the proposed method.

keywords: Multispectral, Image registration, Remote Sensing, High Performance Computing, Parallel Computing, GPU