A Multicondition Normalized BRDF Correction Framework for Inland Water Bodies Using Airborne Hyperspectral Imagery

Abstract

The bidirectional reflectance distribution function (BRDF) effects in airborne hyperspectral images can generate severe brightness gradients and radiometric inconsistency over inland water bodies, hindering precise quantitative applications. This challenge is particularly acute for lightweight airborne hyperspectral systems, where multistrip acquisition and frequent attitude fluctuations can amplify water-surface specular reflection and scattering, resulting in severe radiometric inconsistencies. We propose a unified BRDF correction framework for inland water bodies, driven by the normalization of multiple imaging conditions. By physically decoupling high-frequency sensor attitude fluctuations from periodic sensor heading systemic shifts, and constructing a kernel-based semiempirical model optimized for the complex anisotropic scattering mechanisms of inland water, this framework reduces radiometric distortions induced by dynamic attitude variations and characterizes water-body BRDF properties. By conducting an overall homogeneity visual interpretation of the mosaicked images and quantitatively calculating the reflectance of overlapping regions, we demonstrate that the correction reduces BRDF-induced radiometric inconsistency. Specifically, the mean absolute difference (MAD) of water reflectance in overlapping areas is reduced by over 50%. Evaluated against ground-measured spectra, the coefficient of determination (R ${}^{2}$ ) of the corrected reflectance reaches over 0.9. Compared with traditional BRDF correction methods, the proposed framework achieves superior performance in both visual effect and quantitative evaluation. Through comparative validation from different regions, the robustness and accuracy of the model are further demonstrated, providing precise data support for subsequent high-precision quantitative inversion and mapping. With the trend of lightweight airborne imaging systems, the proposed BRDF correction framework offers a robust radiometric baseline and has extensive application prospects for hyperspectral applications.