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While unsupervised change detection using contrastive learning has beensignificantly improved the performance of literature techniques, at present, itonly focuses on the bi-temporal change detection scenario. Previousstate-of-the-art models for image time-series change detection often usefeatures obtained by learning for clustering or training a model from scratchusing pseudo labels tailored to each scene. However, these approaches fail toexploit the spatial-temporal information of image time-series or generalize tounseen scenarios. In this work, we propose a two-stage approach to unsupervisedchange detection in satellite image time-series using contrastive learning withfeature tracking. By deriving pseudo labels from pre-trained models and usingfeature tracking to propagate them among the image time-series, we improve theconsistency of our pseudo labels and address the challenges of seasonal changesin long-term remote sensing image time-series. We adopt the self-trainingalgorithm with ConvLSTM on the obtained pseudo labels, where we first usesupervised contrastive loss and contrastive random walks to further improve thefeature correspondence in space-time. Then a fully connected layer isfine-tuned on the pre-trained multi-temporal features for generating the finalchange maps. Through comprehensive experiments on two datasets, we demonstrateconsistent improvements in accuracy on fitting and inference scenarios.

Unsupervised CD in satellite image time series by contrastive learning and feature tracking