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

Area of Science:

• Remote Sensing
• Optical Physics
• Materials Science

Background:

• Atmospheric effects significantly degrade the accuracy of polarimetric remote sensing data.
• Accurate estimation of surface properties requires robust atmospheric correction methods.
• Existing methods often rely on calibrated targets, limiting their practical application.

Purpose of the Study:

• To develop and validate a novel method for eliminating atmospheric effects in polarimetric imaging remote sensing.
• To improve the efficiency and accuracy of estimating surface properties, specifically roughness.
• To demonstrate the method's effectiveness under various weather conditions without calibrated targets.

Main Methods:

• Simultaneous detection of ground targets and skylight using polarimetric imagers.
• Application of the skylight division method for enhanced calculation efficiency and accuracy.
• Outdoor experiments to acquire polarimetric bidirectional reflectance distribution functions (pBRDFs) of painted surfaces and skylight.
• Estimation of surface roughness based on corrected polarimetric data.

Main Results:

• The proposed method effectively eliminates atmospheric effects in polarimetric imaging.
• Calculation efficiency is improved via the skylight division method without compromising accuracy.
• pBRDFs of painted surfaces and skylight were successfully obtained under diverse weather conditions.
• Surface roughness estimation accuracy reached 6% under cloudy weather, a significant improvement over 30.72% without correction.

Conclusions:

• The developed method offers a practical and accurate solution for atmospheric correction in polarimetric remote sensing.
• It enables reliable estimation of surface properties, such as roughness, even in challenging atmospheric conditions.
• The technique's independence from calibrated targets broadens its applicability in field measurements.