Adversarial Loss for Semantic Segmentation of Aerial Imagery

• URL: http://arxiv.org/abs/2001.04269v2
• Date: Sat, 18 Jan 2020 10:21:37 GMT
• Title: Adversarial Loss for Semantic Segmentation of Aerial Imagery
• Authors: Clint Sebastian, Raffaele Imbriaco, Egor Bondarev, Peter H.N. de With
• Abstract summary: We propose a novel loss function that learns to understand both local and global contexts for semantic segmentation. The newly proposed loss function deployed on the DeepLab v3+ network obtains state-of-the-art results on the Massachusetts buildings dataset.
• Score: 12.241693880896348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic building extraction from aerial imagery has several applications in urban planning, disaster management, and change detection. In recent years, several works have adopted deep convolutional neural networks (CNNs) for building extraction, since they produce rich features that are invariant against lighting conditions, shadows, etc. Although several advances have been made, building extraction from aerial imagery still presents multiple challenges. Most of the deep learning segmentation methods optimize the per-pixel loss with respect to the ground truth without knowledge of the context. This often leads to imperfect outputs that may lead to missing or unrefined regions. In this work, we propose a novel loss function combining both adversarial and cross-entropy losses that learn to understand both local and global contexts for semantic segmentation. The newly proposed loss function deployed on the DeepLab v3+ network obtains state-of-the-art results on the Massachusetts buildings dataset. The loss function improves the structure and refines the edges of buildings without requiring any of the commonly used post-processing methods, such as Conditional Random Fields. We also perform ablation studies to understand the impact of the adversarial loss. Finally, the proposed method achieves a relaxed F1 score of 95.59% on the Massachusetts buildings dataset compared to the previous best F1 of 94.88%.

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