Self-Supervised Change Detection by Fusing SAR and Optical Multi-Temporal Images

The availability of multi-sensor data presents an opportunity for change detection based on the complementary use of properties associated with different data sources. This paper proposes a new unsupervised change detection framework based on the joint use of SAR and optical images. The framework exploits a contrastive learning algorithm and the assumption of the scarcity of relevant changes. The proposed architecture is a pseudo-Siamese network, which is trained to regress the feature vector of bi-temporal concatenated SAR-optical input data. The output feature vectors from two branches of the pseudo-Siamese network are used to calculate change intensity maps. Then, the binary change map is obtained by setting a proper threshold. The proposed method is validated by using a multi-sensor dataset made up of Sentinel-1 and Sentinel-2 images that is also compared with the single use of each modality image. Experimental results demonstrate improvements of the multisensor approach over single modality and confirm the potentiality of the joint use of SAR and optical images in change detection.