Road Extraction from Remote Sensing Images Using Parallel Softplus Networks

Abstract

Road extraction from remote sensing images plays an important role in traffic management, urban planning, automatic vehicle navigation and emergency management. It is a hot issue that how to extract effectively road information from remote sensing images. Here, a new model, namely parallel softplus network (PSNet), has been proposed, which uses parallel network structure and softplus activation function. Specially, the model uses a new weight initialization for extraction effectiveness. Moreover, compared with the popular models, it extracts more complete and continuous road information on the same road remote sensing images. Meanwhile, it outperforms other extraction models, with a high F1-score. Experimental results indicate that it is a promising model, which effectively extracts road information from remote sensing images with a little noise.