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• W4386160455 abstract "As an important task in remote sensing image analysis, remote sensing change detection (RSCD) aims to identify changes of interest in a region from spatially co-registered multi-temporal remote sensing images, so as to monitor the local development. Existing RSCD methods usually formulate RSCD as a binary classification task, representing changes of interest by merely feature concatenation or feature subtraction and recovering the spatial details via densely connected change representations, whose performances need further improvement. In this paper, we propose STNet, a RSCD network based on spatial and temporal feature fusions. Specifically, we design a temporal feature fusion (TFF) module to combine bitemporal features using a cross-temporal gating mechanism for emphasizing changes of interest; a spatial feature fusion module is deployed to capture fine-grained information using a cross-scale attention mechanism for recovering the spatial details of change representations. Experimental results on three benchmark datasets for RSCD demonstrate that the proposed method achieves the state-of-the-art performance. Code is available at https://github.com/xwmaxwma/rschange." @default.
• W4386160455 created "2023-08-26" @default.
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• W4386160455 date "2023-07-01" @default.
• W4386160455 modified "2023-10-16" @default.
• W4386160455 title "STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images" @default.
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• W4386160455 doi "https://doi.org/10.1109/icme55011.2023.00375" @default.
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