粗糙表面双向反射分布函数优化建模与验证

The bidirectional reflectance distribution function (BRDF) of target surface can be used to describe the scattering characteristics of targets, which has been widely used in the applications such as target detection and identification, feature analysis and extraction. In this paper, based on five parameter BRDF model, two simplified five parameter models (BRDF model of semi glossy material and BRDF model of rough surface material) arc presented according to the characteristics of different target surface material attributes. Furthermore, the model parameter fitting algorithm is optimized. The simulated annealing algorithm (SA) is adopted in the particle swarm optimization algorithm (PSO) to enhance the local search ability of the algorithm and improve the efficiency of the algorithm. The BRDF data of 2024 aluminum alloy samples with different roughnesses and three kinds of large roughness samples arc experimentally measured. The trend of the BRDF variation of target sample with scattering angle, the relationship between the BRDF of target sample and surface roughness, arc analyzed. Simulated annealing particle swarm optimization algorithm (SAPSO), genetic algorithm (GA) and particle swarm algorithm arc used to fit the experimental data. The comparison between the model calculation results and the experimental results shows that the simulated annealing particle swarm algorithm has a smaller fitting error and is more suitable for BRDF optimization modeling, which proves the effectiveness of the algorithm.