Radiometric Normalization Using a Pseudo-Invariant Polygon Features-Based Algorithm with Contemporaneous Sentinel-2A and Landsat-8 OLI Imagery

As sensor parameters and atmospheric conditions create uncertainties for at-sensor radiation detection, radiometric consistency among satellite images is difficult to achieve. Relative radiometric normalization is a method that can improve multi-image consistency with accurate pseudo-invariant features (PIFs), especially over large areas or long time series satellite images. Although there are algorithms that manually or automatically select PIFs, the spatial mismatch of satellite images can affect PIF extraction, particularly with artificial pixels. To alleviate this problem, we proposed to use Landsat-8 OLI as the reference image and Sentinel-2A as the subject image, to apply pseudo-invariant features-based algorithms with polygon features through the single-band and multiple-band regression. Compared to pseudo-invariant point features, hyperspectral library, and histogram matching approaches, the results demonstrate the superiority of the proposed algorithms with correlation coefficients of 0.9948 and 0.9945, and an RMSE of 0.0097 and 0.0095 with multiple- and single-band regression, respectively. We also found more accurate linear fitting and better shape matching through band scattering and reflectance frequency analysis. The proposed algorithms are a significant improvement in radiometric normalization, within artificial pixels, achieving spectral signature consistency.