| 总结: | Differences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switching, and drone platform jitter. However, they can be addressed using hyperspectral image registration. This article proposes a new coarse-to-fine remote sensing image registration framework based on feature and optical flow theory, comparing its performance with that of existing registration algorithms using the same dataset. The proposed method increases the structure similarity index by 5.2 times, reduces the root mean square error by 3.1 times, and increases the mutual information by 1.9 times. To meet the real-time processing requirements of the AOTF spectrometer in remote sensing, a development environment using VS2023+CUDA+OPENCV was established to improve the demons registration algorithm. The registration algorithm for the central processing unit+graphics processing unit (CPU+GPU) achieved an acceleration ratio of ~30 times compared to that of a CPU alone. Finally, the real-time registration effect of spectral data during flight was verified. The proposed method demonstrates that AOTF hyperspectral imagers can be used in real-time remote sensing applications on unmanned aerial vehicles.
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