Unsupervised Building Change Detection in Multi-Modal Sar Images Using Cyclegan

In the literature, Change Detection (CD) methods use the information from heterogeneous sensors to detect the changes more effectively with respect to single-sensor methods. Among them, Deep Learning (DL) CD methods try to learn a common feature domain or perform a domain adaptation, but many still present domain gaps between multi-sensor feature maps. We propose a DL CD method that learns multiscale feature maps in a domain common to multi-modal SAR images using a Cycle Generative Adversarial Network (CycleGAN). We apply a code-alignment loss function to reduce the domain gap between the feature maps derived from the multi-sensor SAR images. The multi-scale feature maps of the CycleGAN generators are processed to derive the change map. Preliminary experiments performed by processing bi-temporal SAR images acquired from COSMO-SkyMed and SAOCOM showed promising results.