A Triple-Branch Architecture With Multiscale Attention for Spatiotemporal Remote Sensing Fusion (opens in new tab)

Existing remote sensing spatiotemporal fusion methods often suffer from excessive smoothing at object boundaries, insufficient modeling of global contextual information, and underutilization of temporal change features. To improve these issues, a triple-branch convolutional neural network is proposed, consisting of a dual-stream spatial network and a temporal difference network. Within the spatial branches, a spatiotemporal adaptive modulation (STAM) module is integrated, in which spatial att...