Light encoder–decoder network for road extraction of remote sensing images

Abstract

Extracting roads from remote sensing images is an important task in the remote sensing field. We propose an approach of designing a light encoder–decoder network for road extraction. We analyze the relationship between road features and the receptive field of encoder–decoder networks and point out that the light encoder–decoder network can be achieved by controlling its receptive field. Based on this, we design road extraction networks on the architecture of a general encoder–decoder network, according to data specifications. In addition, we propose an adaptive weighted binary cross-entropy loss function to solve the problem of data imbalance in the training process. We validate our approach on the Massachusetts roads dataset and the DeepGlobe road extraction dataset. The experimental results show that our method reduces 98% and 94% of the parameters, respectively, compared with general encoder–decoder networks, whereas the performance of road extraction keeps well. Our approach has fewer parameters and good performance, so it is easier to deploy on mobile platforms.