Automatic Road Segmentation from High Resolution Satellite Images Using Encoder-Decoder Network

Abstract

Road network segmentation from high resolution satellite imagery have profound applications in remote sensing. They facilitate for transportation, GPS navigation and digital cartography. Most recent advances in automatic road segmentation leverage the power of networks such as fully convolutional networks and encoder-decoder networks. The main disadvantage with these networks is that they contain deep architectures with large number of hidden layers to account for the lost spatial and localization features. This will add a significant computational overhead. It is also difficult to segment roads from other road-like features. In this paper, we propose a road segmentation architecture with an encoder and two path decoder modules. One path of the decode module approximates the coarse spatial features using upsampling network. The other path uses Atrous spatial pyramid pooling module to extract multi scale context information. Both the decoder paths are combined to fine tune the segmented road network. The experiments on the Massachusetts roads dataset show that our proposed model can produce precise segmentation results than other state-of-the-art models without being computationally expensive.