Dual Lightweight Network with Attention and Feature Fusion for Semantic Segmentation of High-Resolution Remote Sensing Images

Semantic segmentation of high-resolution remote sensing (HRRS) images has been a long-term research topic in the field of remote sensing. Nowdays, many excellent networks based on deep learning have been applied in various remote sensing fields. However, these networks always have a large number of network parameters and rely on extensive computing resources. To solve the above problems, we propose a lightweight dual branches network with the attention modules and the feature fusion module. The backbone networks of dual branches, which have fewer parameters, are used to obtain the detail information and the context information respectively. The attention modules are used to establish full-image dependencies over the local feature representations. The feature fusion module is used to fuse the low-leve features and the high-leve features effectively. Compared to other popular networks, our network has better results evaluated on ISPRS Vaihingen Dataset while with fewer parameters(8M).