Semantic Labeling for High-Resolution Aerial Images Based on the DMFFNet

Semantic labeling in high-resolution aerial images is important for its wide range of applications. In this paper, we propose an end-to-end dual multi-scale feature fusion network (DMFFNet) for high-resolution aerial multi-source images. DMFFNet aims to further improve the semantic labeling results of the region where the multispectral features are indistinguishable. Specifically, we design a channel fusion strengthen (CFS) module, which can fuse features adaptively by modelling interdependencies between channels. Furthermore, a multiscale context aggregation (MCA) module is utilized to obtain larger receptive field and more contextual information. The experiment results confirm the DMFFNet with CFS and MCA improve the semantic labeling performance by utilizing multi-source data.