Auto robust relative radiometric normalization via latent change noise modelling

TL;DR

This paper introduces a robust radiometric normalization method using latent change noise modeling, improving change detection accuracy in satellite imagery by effectively handling object changes and noise.

Contribution

It proposes a probabilistically grounded, noise-based RRN model with a mixture of Gaussian noise, enhancing robustness against clouds, fogs, and changes, and integrating a new evaluation indicator.

Findings

HM-RRN-MoG achieves the best performance among tested models.

The method robustly handles clouds, fogs, and object changes.

It improves change detection accuracy and reduces pseudo-changes.

Abstract

Relative radiometric normalization(RRN) of different satellite images of the same terrain is necessary for change detection, object classification/segmentation, and map-making tasks. However, traditional RRN models are not robust, disturbing by object change, and RRN models precisely considering object change can not robustly obtain the no-change set. This paper proposes auto robust relative radiometric normalization methods via latent change noise modeling. They utilize the prior knowledge that no change points possess small-scale noise under relative radiometric normalization and that change points possess large-scale radiometric noise after radiometric normalization, combining the stochastic expectation maximization method to quickly and robustly extract the no-change set to learn the relative radiometric normalization mapping functions. This makes our model theoretically grounded regarding the probabilistic theory and mathematics deduction. Specifically, when we select histogram matching as the relative radiometric normalization learning scheme integrating with the mixture of Gaussian noise(HM-RRN-MoG), the HM-RRN-MoG model achieves the best performance. Our model possesses the ability to robustly against clouds/fogs/changes. Our method naturally generates a robust evaluation indicator for RRN that is the no-change set root mean square error. We apply the HM-RRN-MoG model to the latter vegetation/water change detection task, which reduces the radiometric contrast and NDVI/NDWI differences on the no-change set, generates consistent and comparable results. We utilize the no-change set into the building change detection task, efficiently reducing the pseudo-change and boosting the precision.