DFC-UNet: A U-Net-Based Method for Road Extraction From Remote Sensing Images Using Densely Connected Features

Road extraction from high-resolution remote sensing images is a challenging task due to the presence of disturbing features and the diversity of road representations. To overcome these problems, deep neural networks-based methods have been recently used to improve the speed and accuracy of road extraction. In this letter, we propose a simple yet effective method for feature extraction with context fusion and self-learning sampling, which we call dual feature fusion (DFF). Moreover, we point out that the DFF method is functionally similar to the downsampling and upsampling structure. From this, we propose a network with a dense feature skip connect structure (DFC-UNet) to extract the roads from remote sensing images. The complexity of the high-dimensional features of the U-shaped structure is also analyzed, and the redundant features are suppressed through the equivalent replacement of the DFF block. Aiming at the unbalanced characteristics of samples and the topological characteristics of the road network, we then propose a comprehensive loss function based on dynamic weighting to strengthen the learning of the road network. Experimental results on the Massachusetts road dataset, the DeepGlobe dataset, and the CHN6_CUG dataset confirm the effectiveness of the proposed method.