TransRoadNet: A Novel Road Extraction Method for Remote Sensing Images via Combining High-Level Semantic Feature and Context
Zhigang Yang, Daoxiang Zhou, Ying Yang, Jiapeng Zhang, Zehua Chen
Abstract
Road extraction is a significant research hotspot in the area of remote sensing images. Extracting an accurate road network from remote sensing images is still challenging, because some objects in the images are similar to the road, and some results are discontinuous due to the occlusion. Recently, convolutional neural networks (CNNs) have shown their power in a road extraction process. However, the contextual information cannot be captured effectively by those CNNs. Based on CNNs, combining with high-level semantic features and foreground contextual information (FCI), a novel road extraction method for remote sensing images is proposed in this letter. First, the position attention (PA) mechanism is designed to enhance the expression ability for the road feature. Then, the contextual information extraction module (CIEM) is constructed to capture the road contextual information in the images. At last, an FCI supplement module (FCISM) is proposed to provide foreground context information at different stages of the decoder, which can improve the inference ability for the occluded area. Extensive experiments on the DeepGlobal road dataset showed that the proposed method outperforms the existing methods in accuracy, intersection over union (IoU), precision, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score and yields competitive recall results, which demonstrated the efficiency of the new model.