A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images

Abstract

Road segmentation from high-resolution visible remote sensing images provides an effective way for automatic road network forming. Recently, deep learning methods based on convolutional neural networks (CNNs) are widely applied in road segmentation. However, it is a challenge for most CNN-based methods to achieve high segmentation accuracy when processing high-resolution visible remote sensing images with rich details. To handle this problem, we propose a road segmentation method based on a Y-shaped convolutional network (indicated as Y-Net). Y-Net contains a two-arm feature extraction module and a fusion module. The feature extraction module includes a deep downsampling-to-upsampling sub-network for semantic features and a convolutional sub-network without downsampling for detail features. The fusion module combines all features for road segmentation. Benefiting from this scheme, the Y-Net can well segment multi-scale roads (both wide and narrow roads) from high-resolution images. The testing and comparative experiments on a public dataset and a private dataset show that Y-Net has higher segmentation accuracy than four other state-of-art methods, FCN (Fully Convolutional Network), U-Net, SegNet, and FC-DenseNet (Fully Convolutional DenseNet). Especially, Y-Net accurately segments contours of narrow roads, which are missed by the comparative methods.