  Existing remote sensing change detection methods are heavily affected by
seasonal variation. Since vegetation colors are different between winter and
summer, such variations are inclined to be falsely detected as changes. In this
letter, we proposed an image translation method to solve the problem. A
style-based recalibration module is introduced to capture seasonal features
effectively. Then, a new style discriminator is designed to improve the
translation performance. The discriminator can not only produce a decision for
the fake or real sample, but also return a style vector according to the
channel-wise correlations. Extensive experiments are conducted on
season-varying dataset. The experimental results show that the proposed method
can effectively perform image translation, thereby consistently improving the
season-varying image change detection performance. Our codes and data are
available at https://github.com/summitgao/RSIT_SRM_ISD.
