BRDF Sampling from Hyperspectral Images: A Proof of Concept

Materials represented by measured BRDF (Bidirectional Reflectance Distribution function) with reflectance data captured from real-world materials have become increasingly prevalent due to the development of novel measurement approaches. Nowadays, important limitations can be highlighted in the current material scanning process, mostly related to the high diversity of existing materials in the real-world and the tedious process for material scanning. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach is proposed for modelling the material appearance by sampling hyperspectral measurements on the BRDF domain. An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. The generated hyperspectral data cubes were used to identify materials with a similar spectral behaviour. Then, a sparse mapping of collected samples is developed to study the appearance of natural and artificial materials in an urban scenario.