Abstract: 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. 

Key words: hyperspectral, radiometric correction algorithms, push-broom sensors, unmanned aerial vehicle (UAV)