论文标题
用于从卫星图像建筑损害评估的跨方向特征融合网络
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
论文作者
论文摘要
当自然灾害（例如地震，飓风等）罢工时，需要快速有效的反应。在进行有效响应之前，卫星图像的建筑损害评估至关重要。高分辨率的卫星图像提供了丰富的信息，并提供了灾前和后的场景，以进行分析。但是，大多数现有的作品只是将前和后的图像用作输入而无需考虑其相关性。在本文中，我们提出了一种新型的跨方向融合策略，以更好地探索前和迪沙斯特图像之间的相关性。此外，还利用数据增强方法cutmix来应对硬班的挑战。所提出的方法在大规模建筑物损害评估数据集-XBD上实现了最先进的性能。
Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before an effective response is conducted. High-resolution satellite images provide rich information with pre- and post-disaster scenes for analysis. However, most existing works simply use pre- and post-disaster images as input without considering their correlations. In this paper, we propose a novel cross-directional fusion strategy to better explore the correlations between pre- and post-disaster images. Moreover, the data augmentation method CutMix is exploited to tackle the challenge of hard classes. The proposed method achieves state-of-the-art performance on a large-scale building damage assessment dataset -- xBD.
