SemiSiROC: Semisupervised Change Detection With Optical Imagery and an Unsupervised Teacher Model

Abstract

Change detection (CD) is an important yet challenging task in remote sensing. In this article, we underline that the combination of unsupervised and supervised methods in a semisupervised framework improves CD performance. We rely on half-sibling regression for optical change detection (SiROC) as an unsupervised teacher model to generate pseudolabels (PLs) and select only the most confident PLs for pretraining different student models. Our results are robust to three different competitive student models, two semisupervised PL baselines, two benchmark datasets, and a variety of loss functions. While the performance gains are highest with a limited number of labels, a notable effect of PL pretraining persists when more labeled data are used. Further, we outline that the confidence selection of SiROC is indeed effective and that the performance gains generalize to scenes that were not used for PL training. Through the PL pretraining, SemiSiROC allows student models to learn more refined shapes of changes and makes them less sensitive to differences in acquisition conditions.