Remote Sensing Image Translation via Style-Based Recalibration Module and Improved Style Discriminator

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 the season-varying data set. 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.

Automated summary

The existing remote sensing change detection methods are often affected by seasonal variations, leading to false detections. The proposed image translation method uses a style-based recalibration module and a new style discriminator to improve the detection performance significantly.