Rotation Consistency-Preserved Generative Adversarial Networks for Cross-Domain Aerial Image Semantic Segmentation

Due to its wide applications, aerial image semantic segmentation attracts increasing research interest in recent years. As well known, deep semantic segmentation network (DSSN) has been widely used to deal with aerial image segmentation and achieves spectacular success. However, when applying the DSSN trained with the labeled aerial images (i.e., the source domain) to predict the aerial images acquired with different acquisition conditions (i.e., the target domain), the performance often dramatically degrades. To alleviate the negative influence of cross-domain data shift, this paper proposes a domain adaptation approach to deal with cross-domain aerial image semantic segmentation. More precisely, this paper proposes a novel rotation consistency-preserved generative adversarial network (RCP-GAN) to carry out domain adaptation for mapping aerial images in the source domain to the target domain. Furthermore, the mapped aerial imageries with labels are used to train DSSN, which is further used to classify aerial imagery in the target domain. To verify the validity of the presented approach, we give two cross-domain experimental settings including: (I) variation of geographic location; (II) variation of both geographic location and imaging mode. Extensive experiments under two typical cross-domain settings show that our proposed method can effectively address the domain shift problem and outperform the state-of-the-art methods with a large margin.