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

Abstract

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.

LIRRN: Location-Independent Relative Radiometric Normalization of Bitemporal Remote-Sensing Images

“…However, when dealing with image pairs that include significant land cover/land use (LULC) changes, these methods may introduce noise structures and artifacts to the final results because of their equal treatment of all the pixels. SRRN methods, however, are specifically designed to handle radiometric distortions in such image pairs by extracting pseudo-invariant features (PIFs) and using them to establish a more precise MF [6,7,10,11].…”

“…Despite promising results, these methods primarily rely on iteratively identifying PIFs to re-estimate parameters for aligning images. A more advanced SRRN method was recently proposed by Chen et al [10], in which PIFs in the shape of polygons were used to form an RRN model and generate reliable normalized images. Although these methods have shown promising results in RRN, they are limited to working with geo/coregistered image pairs and are therefore incompatible when unregistered input images need to be normalized [19].…”

A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica

“…Li et al [36] proposed a series of local linear model approaches and a specific global cost function that can correct global and local color discrepancies simultaneously and preserve image gradient as much as possible. Chen et al [37] proposed pseudo-invariant feature-based algorithms with polygon features through single-band and multiple-band regression, and applied them to radiometric normalization between Sentinel-2A and Landsat-8 OLI images. The effectiveness of the proposed algorithm is demonstrated by comparing the results with the histogram matching method.…”

“…Different from the method that fits the pixel-to-pixel features of overlapping region images as previously mentioned, histogram matching is a distribution-based color consistency matching method [19,40]. It can avoid subjectivity in the selection of pseudo point features and image misregistration [37]. The focus of this algorithm is on the digital number (DN) distribution between the subject image and the target image, which is closely linked to the type of terrain objects on the image.…”