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Efficient Multi-Class Semantic Segmentation of High Resolution Aerial Imagery with Dilated LinkNet

In this paper, we address the problem of multi-class semantic segmentation of high resolution aerial imagery with a deep-convolutional-neural-network-based model named D-LinkNet, which was initially designed for the task of road extraction. D-LinkNet extends LinkNet, which is considered as an efficient method for semantic segmentation and adopts encoder-decoder architecture, by inserting dilated convolution layers between LinkNets encoder and decoder to enlarge the receptive field of kernel without reducing the resolution of the feature maps. Besides, we use the multi-class form of the original loss function combined by Dice loss and Binary Cross Entropy loss, which has proven to be effective in classification tasks with imbalanced class distribution that is prevalent for aerial imagery. Experimental results on public available Potsdam dataset demonstrate that D-LinkNet obtains an overall accuracy of 86.1%, which outperforms state-of-the-art methods. Moreover, D-LinkNet achieves a competitive speed in the experiment so it can produce relatively accurate predictions more efficiently.