While unsupervised change detection using contrastive learning has been
significantly improved the performance of literature techniques, at present, it
only focuses on the bi-temporal change detection scenario. Previous
state-of-the-art models for image time-series change detection often use
features obtained by learning for clustering or training a model from scratch
using pseudo labels tailored to each scene. However, these approaches fail to
exploit the spatial-temporal information of image time-series or generalize to
unseen scenarios. In this work, we propose a two-stage approach to unsupervised
change detection in satellite image time-series using contrastive learning with
feature tracking. By deriving pseudo labels from pre-trained models and using
feature tracking to propagate them among the image time-series, we improve the
consistency of our pseudo labels and address the challenges of seasonal changes
in long-term remote sensing image time-series. We adopt the self-training
algorithm with ConvLSTM on the obtained pseudo labels, where we first use
supervised contrastive loss and contrastive random walks to further improve the
feature correspondence in space-time. Then a fully connected layer is
fine-tuned on the pre-trained multi-temporal features for generating the final
change maps. Through comprehensive experiments on two datasets, we demonstrate
consistent improvements in accuracy on fitting and inference scenarios.

本文提出了一种使用对比学习和特征跟踪进行无监督卫星图像时间序列变化检测的方法，并通过两个数据集的实验证明了该方法的准确性提升。