Auto-Robust Relative Radiometric Normalization via Latent Change Noise Modeling

Relative radiometric normalization (RRN) of different satellite images of the same terrain is essential for change detection, object classification/segmentation, and map-making tasks. However, identifying increasingly comprehensive no-change (NC) points for better normalization generalization is crucial. Traditional RRN models struggle to robustly derive the complete set of NC points, often being disrupted by object changes. Additionally, RRN models that account for object changes precisely may require human involvement and instructions. This article proposes automated and robust RRN methods through latent change noise modeling. They capitalize on the observation that NC points exhibit small-scale noise under RRN, while change points exhibit large-scale noise after normalization. The proposed model benefits from a strong theoretical foundation grounded in probabilistic theory and mathematical deduction. Specifically, we integrate histogram matching for relative radiometric normalization with mixture of Gaussian noise (HM-RRN-MoG) as the RRN learning scheme. This model demonstrates effective performance and has been successfully applied to hundreds of image production tasks. HM-RRN-MoG has impressive NC prediction accuracy (70%) and superior RRN performance compared to the state-of-the-art models. Furthermore, it exhibits robust behavior against clouds and changes. Our method also offers a natural and valuable evaluation metric for RRN: the root-mean-square error within the NC set. Applying the HM-RRN-MoG model to vegetation/water change detection reduces radiometric contrast and normalized difference vegetation index (NDVI)/normalized difference water index (NDWI) differences within the NC set, leading to more consistent and comparable results. Similarly, in the building change detection task, utilizing the NC set effectively reduces pseudo-changes and boosts precision. The code is available at
https://github.com/647LiuSQ/HM-MoG-RRN/tree/main
.