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

22

Citations

41

References

2024

Year

Abstract

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 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}^{2}$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}^{2}$ </tex-math></inline-formula> CD method outperforms existing techniques, showcasing its superior performance and promising potential. The codes of this work will be available for the sake of reproducibility at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qianday/SSCD-CD</uri> .

References

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