Color-Robust Sea Ice Change Detection

Abstract

Sea ice change detection is vital for understanding climate dynamics and ensuring maritime safety. Existing deep learning methods often struggle with the significant impact of color variations in satellite imagery, which can lead to inaccurate detection results. Moreover, the scarcity of labeled sea ice change data limits the ability of models to generalize across diverse scenarios. To address these challenges, we propose SICNet, a sea ice change detection model with enhanced color robustness and data efficiency. A wavelet-guided color-robust fusion (WCF) module is introduced to reduce low-frequency color discrepancies while preserving high-frequency edge details. In addition, a novel change-sensitive CutMix (CSC) strategy is used to augment training samples by focusing on regions with moderate changes, effectively increasing data diversity. Experiments conducted on our constructed sea ice change dataset demonstrate that SICNet achieves superior performance and robustness under varying environmental and lighting conditions. The source code of SICNet is available at https://github.com/viking-hong/SICNet.git