Road extraction from very high resolution satellite images is one of the most important topics in the field of remote sensing. For the road segmentation problem, spatial properties of the data can usually be captured using Convolutional Neural Networks. However, this approach only considers a few local neighborhoods at a time and has difficulty capturing long-range dependencies. In order to overcome the problem, we propose Non-Local LinkNet with non-local blocks that can grasp relations between global features. It enables each spatial feature point to refer to all other contextual information and results in more accurate road segmentation. In detail, our method achieved 65.00% mIOU scores on the DeepGlobe 2018 Road Extraction Challenge dataset. Our best model outperformed D-LinkNet, 1stranked solution, by a significant gap of mIOU 0.88% with much less number of parameters. We also present empirical analyses on proper usage of non-local blocks for the baseline model.

Face recognition methods have been significantly improved in recent years owing to the advances made in loss functions. Typically, loss functions are designed to enhance the separability power by concentrating on hard samples in mining-based approaches or by increasing the feature margin between different classes in margin-based approaches. However, margin-based methods lack the utilization of informative hard sample, and mining-based methods also fail to learn the latent correlations between classes. Moreover, there are no methods that simultaneously consider the effects of hard samples and feature margin through the same shape of feature angular margin. Therefore, this paper introduces the Angular Margin-Mining Softmax (AMM-Softmax) loss function, which adaptively emphasizes hard samples while also increasing the decision margins. The proposed AMM-Softmax loss function introduces a linear angular margin for hard samples, enabling the direct optimization of the geodesic distance margin and maximization of class separability. Furthermore, the proposed AMM-Softmax loss function is computationally efficient and can be easily converged by rapidly switching from the hard samples to easy samples. The results of the extensive experimental analyses conducted on popular benchmarks demonstrate the superiority of the proposed AMM-Softmax loss function over the existing state-of-the-art methods.

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