Image fusion employing adaptive spectral-spatial gradient sparse regularization in UAV remote sensing

Unmanned aerial vehicle (UAV) remote sensing has been widely used in vegetation phenotypes and precision agriculture. The fusion of UAV multispectral and panchromatic images has considerable research value. For example, an accurate vegetation index can be obtained. However, large geometrical distortions are observed in UAV images, contributing to the insufficiency of existing fusion algorithms. Spectrum consistency, which indicates that changes in spectral direction are always a smooth function, is investigated in this paper to solve the above problem. Spatial adaptivity is also introduced to reduce spectral distortion in the fusion process. Based on the two aspects, a multispectral and panchromatic image fusion model employing adaptive spectral-spatial gradient sparse regularization is proposed for UAV remote sensing. The separable approximation and augmented Lagrangian methods are employed to optimize this model. In the experiments, the proposed method is firstly compared with other state-of-the-art fusion algorithms, and good performance is verified by UAV datasets in terms of visual effect and objective quality analysis. Secondly, the fusion algorithm is applied in the application of a vegetation phenotype. The experiments finally demonstrate that accurate vegetation indices can be generated by adopting the proposed algorithm. This finding proves the substantial research value of the proposed algorithm in UAV remote sensing. © 2019 Elsevier B.V.