Direct reflectance transformation methodology for drone-based hyperspectral imaging

Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone equipped with camera an irradiance sensor, allowing of image pixel values to factors without ground reference data. This method requires sensors calibrated higher accuracy than what usually required by empirical line (ELM), but consequently it offers benefits robustness, ease operation, ability used Beyond-Visual Line Sight flights. The objective this study was develop assess drone-based workflow for implement our system. novel atmospheric correction also introduced, using two panels, but, unlike ELM, not directly affected changes illumination. sensor system consists (Rikola HSI, Senop) onboard spectrometer (FGI AIRS), which were both given thorough calibrations. laboratory tests flight experiment, FGI AIRS tilt-corrected irradiances had better 1.9% at solar zenith angles up 70°. system's low-altitude factor assessed experiment where normalized root mean square errors (NRMSE) less ±2% light panels (25% 50%) ±4% dark (5% 10%). high-altitude images, taken 100–150 m altitude, NRMSEs within 1.4%–8.7% VIS bands 2.0%–18.5% NIR bands. Significant effects appeared already 50 altitude. proposed found practical decreased 1.3%–2.6% 2.3%–5.3% Overall, efficient provide similar accuracies as providing operational advantages such challenging scenarios forest monitoring, large-scale autonomous mapping tasks, real-time applications. Tests varying illumination conditions showed that gravel vegetation targets varied 8% between sunny cloudy due anisotropy effects, while accuracy. suggests have further accounted