无人机搭载的高光谱传感器影像辐射校正算法OA

This study addresses the intra and inter-strip radiometric non-uniformity in UAV(Unmanned Aerial Vehicle)push-broom hyperspectral imagery through an adaptive radiometric correction model based on minimum spectral features.By constructing a black-box model to simulate imaging physics,pixel-wise nonlinear mapping functions referenced to the minimum spectral features is established,synergistically optimizing intra-image spectral consistency and inter-strip radiometric uniformity.Experiments demonstrate that the corrected imagery exhibits radiation intensity fluctuation amplitude reduced to within 5％of original data,with over 90％elimination rates for inter-strip banding artifacts and brightness gradient distortion.The absolute error of total phosphorus concentration inversion in water bodies derived from corrected data remains stably controlled within the 0.05 mg/L precision threshold.The proposed model enhances multi-strip radiometric correction efficiency by establishing nonlinear relationships between spectral radiation characteristics and sensor response,significantly improving engineering applicability for quantitative inversion of push-broom hyperspectral data and providing a reliable radiometric reference for regional-scale hyperspectral remote sensing monitoring.