Unsupervised change detection in multi-modal SAR images using CycleGAN

Many Change Detection (CD) methods exploit the bi-temporal multi-modal data derived by multiple sensors to find the changes effectively. State-of-the-Art CD methods define features with a common domain between the multi-modal data by normalizing input images or ad hoc feature extraction/selection methods. Deep Learning (DL) CD methods automatically learn features with a common domain during the training or adapt the features derived by multi-modal data. However, CD methods focusing on multi-sensor multi-frequency SAR data are still poorly investigated. We propose a DL CD method that exploits a Cycle Generative Adversarial Network (CycleGAN) to automatically learn and extract multi-scale feature maps in a domain common to the input multifrequency multi-sensor SAR data. The feature maps are learned, during unsupervised training, by generators that aim to transform the input data domain into the target one while preserving the semantic information and aligning the feature domain. We process the multi-sensor multi-frequency SAR data with the trained generators to produce bi-temporal multi-scale feature maps that are compared to enhance changes. A standard-deviation-based feature selection is applied to keep only the most informative comparisons and reject the ones with poor change information. The multi-scale comparisons are used for a detail preserving CD. Preliminary experimental results conducted on bi-temporal SAR data acquired by Cosmo-SkyMed and SAOCOM on the urban area of Milan, Italy, in January 2020 and August 2021 provided promising results.