  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 bi-temporal 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.
