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

TL;DR

This paper introduces a style-based image translation method with a recalibration module and an improved style discriminator to address seasonal variations in remote sensing change detection, significantly enhancing translation and detection accuracy.

Contribution

It proposes a novel style-based recalibration module and a style discriminator to improve remote sensing image translation under seasonal variations.

Findings

Effective translation of season-varying images.

Improved change detection performance.

Validated on season-varying dataset.

Abstract

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.