LGNet: Location-Guided Network for Road Extraction From Satellite Images
Jingtao Hu, Junyu Gao, Yuan Yuan, Jocelyn Chanussot, Qi Wang
Abstract
Road connectivity is vital in road extraction for accurate vehicle navigation. However, the segmentation-based methods fail to model the connectivity resulting in broken road segments. Therefore, we propose a Location-Guided Network (LGNet) for promoting connectivity performance in a very effective and efficient way. Specifically, an auxiliary Road Location Prediction (RLP) task is designed to obtain global road connectivity information, which improves the performance of road segmentation. The RLP can predict the location coordinates of the whole roads with row anchors and column anchors. By aggregating the global location context to the segmentation branch with a location-guided decoder (LG-Decoder), the features can finally capture the connectivity of each road segment. Overall, LGNet has the following advantages: 1) The proposed RLP and LCG can plug into any encoder-decoder network and achieve an impressive performance. 2) High computational efficiency. In comparison with the multi-branch method, our proposed LGNet requires about 6× fewer GFLOPs. 3) The superior road connectivity performance. A series of experiments are conducted on two road extraction data sets (SpaceNet and DeepGlobe), confirming the effectiveness of the LGNet.