Conducting the unmanned aerial vehicle (UAV) hyperspectral unmixing of rice and obtaining the hyperspectral reflectance information of rice plants is of great significance for improving the accuracy of the inversion model of rice physical and chemical parameters. Most of the current research is based on the data of hyperspectral remote sensing images themselves for demixing. That is, unmixing of hyperspectral data is carried out by using algorithm model. In this study, the advantages of hyperspectral images and visible spectral images were complemented, and a hyperspectral unmixing method for UAV in rice field was based on the fusion of UAV high-definition images and hyperspectral images remote sensing images was proposed. This method solved the problem of the limitation of single data and enhanced the description ability of spectral data for ground objects. In order to better calculate the endmember abundance, the high-definition digital orthophotos of the target area were spatially aligned with the UAV hyperspectral remote sensing images, so that the pictures obtained by different sensors were aligned in geometric positions. The supervised classification method of the SVM classifier was used to classify the digital orthophoto of visible light, and the result of the classification was used to correspond to a pixel of the hyperspectrum to obtain the endmember abundance within a pixel. Suppose the endmembers of the water body in adjacent areas were the same, the linear unmixing model (LSMM) was used to unmix the mixed pixels in the adjacent area and finally the hyperspectral reflectance information of rice was obtained. The results showed that the spatial registration of the two images enriches the data source information, which was beneficial to the endmember abundance calculation of the pixels. Among them, the unmixing effect of rice endmember abundance above 70% was the best, the unmixing effect of abundance above 50% was general, and the unmixing effect was poor when the abundance was below 30%. Use the supervised classification method to classify the ground objects with an accuracy of 99.5%, and the classification accuracy of the object-oriented method was 98.2%, the supervised classification method was better than the object-oriented classification method. The final decomposition reflectance of the mixed pixel was higher than that of the original mixed pixel, which reduced the influence of the mixed part of the water body on the spectral data, and made the spectral reflectance of the rice after decomposition more accurate. This research could provide a theoretical basis for the inversion of the UAV imaging hyperspectral remote sensing of rice physical and chemical parameters.