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As an important task in remote sensing image analysis, remote sensing changedetection (RSCD) aims to identify changes of interest in a region fromspatially co-registered multi-temporal remote sensing images, so as to monitorthe local development. Existing RSCD methods usually formulate RSCD as a binaryclassification task, representing changes of interest by merely featureconcatenation or feature subtraction and recovering the spatial details viadensely connected change representations, whose performances need furtherimprovement. In this paper, we propose STNet, a RSCD network based on spatialand temporal feature fusions. Specifically, we design a temporal feature fusion(TFF) module to combine bi-temporal features using a cross-temporal gatingmechanism for emphasizing changes of interest; a spatial feature fusion moduleis deployed to capture fine-grained information using a cross-scale attentionmechanism for recovering the spatial details of change representations.Experimental results on three benchmark datasets for RSCD demonstrate that theproposed method achieves the state-of-the-art performance. Code is available athttps://github.com/xwmaxwma/rschange.

STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images