Global context based automatic road segmentation via dilated convolutional neural network.

Road segmentation from remote sensing images is a critical task in many applications. In recent years, various approaches, particularly deep learning-based methods, have been proposed for accurate road segmentation. However, most existing road segmentation methods always obtain unsatisfactory results (e.g., heterogeneous pixels) due to the complex backgrounds and view occlusions of buildings and trees around a road; consequently, road segmentation remains a challenging problem. In this study, we propose a novel global context based dilated convolutional neural network (GC-DCNN) to address the aforementioned problem. The structure of GC-DCNN is similar to that of UNet. In particular, building the encoder of GC-DCNN with three residual dilated blocks is suggested to further enlarge the effective receptive field and learn additional discriminative features. Thereafter, a pyramid pooling module is used to capture the multiscale global context features and fuse them to achieve stronger feature representation. The decoder network upsamples the fused features to the same size as the input image, combining the high-resolution features with the contracting path of the encoder network. Moreover, the dice coefficient loss is adopted as the loss function. This function differs from those in most previous studies but is more suitable for road segmentation. Extensive experimental results on two benchmark datasets compared with several baseline models demonstrate the superiority of the proposed GC-DCNN algorithm.