Improved atmospheric effect elimination method for the roughness estimation of painted surfaces

We propose a method for eliminating the atmospheric effect in polarimetric imaging remote sensing by using polarimetric imagers to simultaneously detect ground targets and skylight, which does not need calibrated targets. In addition, calculation efficiencies are improved by the skylight division method without losing estimation accuracy. Outdoor experiments are performed to obtain the polarimetric bidirectional reflectance distribution functions of painted surfaces and skylight under different weather conditions. Finally, the roughness of the painted surfaces is estimated. We find that the estimation accuracy with the proposed method is 6% on cloudy weather, while it is 30.72% without atmospheric effect elimination.