SCEDNet: A Style Consistency Enhanced Differential Network for Remote Sensing Image Change Detection

Change detection in remote sensing images is crucial for assessing human activity impacts and supporting government decision-making. However, in practice, obtaining bi-temporal remote sensing images with consistent conditions is highly limited, and existing change detection methods still face two main challenges: 1) In real-world scenarios, inconsistent sensor and lighting conditions cause significant style (visual appearance) differences between bi-temporal remote sensing images, leading to false changes and reducing change detection accuracy. 2) Remote sensing images contain complex semantic information, and complex scenarios such as shadow occlusion and seasonal vegetation changes make the existing methods difficult to capture relevant features with related to change areas. To address these challenges, we propose a style consistency enhanced differential network (SCEDNet) to eliminate style discrepancies between temporally distinct images and enhance the semantic information of change features. Specifically, we introduce a style consistency module (SCM) in the encoder to extract consistent features by computing the mean and variance of temporal features. Then, we introduce an enhanced differential module (EDM) to enhance change semantics, tackling issues like mislocalization and incomplete regions in complex cases such as shadow occlusion and seasonal vegetation changes. Additionally, we design a gate fusion up-sampling (GFU) and change refine module (CRM) in the decoder to integrate multi-level differential features with different semantic information and highlight key changes, further improving change detection performance. Experiments on the CDD and GZ_CD datasets show that SCEDNet outperformed eight methods, achieving F1-scores of 95.59% and 90.41%, respectively. Code and datasets are available at https://github.com/Yzwfff/SCEDNet.