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Efficient Remote Sensing Change Detection With Change State Space Models
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AI
DOI：10.1109/LGRS.2025.3629303.png)
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Abstract
En 中文
ConvNets and Vision Transformers (ViTs) have been widely used for change detection (CD), though they exhibit limitations: long-range dependencies are not effectively captured by the former, while the latter are associated with high computational demands. Vision Mamba, based on State Space Models, has been proposed as an alternative, yet has been primarily utilized as a feature extraction backbone. In this work, the change state space model (CSSM) is introduced as a task-specific approach for CD, designed to focus exclusively on relevant changes between bitemporal images while filtering out irrelevant information. Through this design, the number of parameters is reduced, computational efficiency is improved, and robustness is enhanced. CSSM is evaluated on three benchmark datasets, where superior performance is achieved compared to ConvNets, ViTs, and Mamba-based models, at a significantly lower computational cost. The code will be made publicly available at https://github.com/Elman295/CSSM upon acceptance
Keywords:
Change detection (CD)
mamba
optical remote sensing
state space model
Journal
I
IF:
4.4
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585
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0