Summator–Subtractor Network: Modeling Spatial and Channel Differences for Change Detection

作者：Leiquan Wang, Fang Ye, Zhongwei Li, Chunlei Wu, Mingming Xu, Mingwen Shao · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2024 · DOI：10.1109/tgrs.2024.3349638 · 被引用次数：23 · 研究领域：Remote-Sensing Image Classification、Remote Sensing and Land Use、Remote Sensing in Agriculture

The field of remote sensing (RS) image change detection (CD) has made significant progress, largely due to the powerful feature representation abilities of deep learning. However, traditional methods have not fully exploited the valuable information in differences. These methods often treat deep models as tools to extract features from individual images, which limits their ability to effectively describe differences. Additionally, many approaches tend to focus on spatial differences, while neglecting variations in the channel dimension. In this study, we introduce a novel Summator–Subtractor network for CD (${S}^{2}$CD), which adeptly captures subtle differences within both the spatial and channel aspects of bi-temporal images. The initial spatial and channel differences are derived through summation and subtraction operations on the bi-temporal images. The summator computes initial channel variations, while the subtractor captures initial spatial disparities. Transformers are then used to pull out meaningful differences in both spatial and channel patterns, allowing for a more nuanced understanding than methods relying solely on features from individual images. Finally, a heterogeneous modulation block integrates channel and spatial difference features, thus amplifying overall differences. Through extensive experimentation on four widely acknowledged CD benchmark datasets, our proposed${S}^{2}$CD method outperforms existing techniques, showcasing its superior performance and...