LEARNING TRANSFORMATIONS BETWEEN HETEROGENEOUS SAR AND OPTICAL IMAGES FOR CHANGE DETECTION

Change detection based on heterogeneous images is challenging because of the distribution variance caused by imaging properties of different types of sensor. Most methods deal with this problem by transforming features into a common space. However, the lack of available labeled data limits the training of complex models and representation of heterogeneous distributions. In this paper, we propose to train a network via abundant unlabeled data by adopting cyclic adversarial pre-training in order to learn the relationship between heterogeneous distributions. After pre-training, for change detection, we introduce a constraint to maintain consistency of image content, to avoid the participation of changed pixels in training. Experiments on heterogeneous optical and SAR images prove the effectiveness of our proposed method.