Relative Radiometric Normalisation of Unmanned Aerial Vehicle Photogrammetry-based RGB Orthomosaics

Abstract

The problem of brightness differences between images of the same scene is important to the field of unmanned aerial vehicle (UAV) photogrammetry and affects both the aesthetics and the interpretation of the final product. This problem can be caused by changes in the positions of the camera or sun, as well as weather conditions. This article deals with the problem of varied image brightness caused by the latter. Relative radiometric normalisation (RRN) of acquired RGB imagery is used to diminish the effects of this phenomenon and improve the visual quality of the resultant product. The presented algorithm considers the specificity of UAV-acquired data. It utilises image positions and their relationships to group similar images, choose references and perform RRN via histogram matching. The final method is robust and fully automatic. Validation performed on two independent datasets confirms its effectiveness both qualitatively (improving the appearance of the orthomosaic) and quantitatively.