一种空间掩码自适应门控网络用于利用SAR和不完整多光谱数据的多模态洪水后水 extent 映射
A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data
AI总结：
本文提出SMAGNet，一种多模态深度学习模型，通过融合SAR和不完整多光谱数据，提升洪水后水 extent 映射的准确性和鲁棒性。
摘要：
在洪水事件中映射水 extent 是灾难管理各阶段（缓解、准备、响应和恢复）中至关重要的环节。特别是在响应阶段，当及时准确的信息至关重要时，合成孔径雷达（SAR）数据被主要用来生成水 extent 地图。最近，通过多模态方法利用SAR和多光谱（MSI）数据的互补特性，已成为利用深度学习模型推进水 extent 映射的有前景策略。这种策略在及时的洪水后观测有限时特别有益，因为它使能够利用所有可用的影像数据以获得更准确的洪水后水 extent 映射。然而，将部分可用的MSI数据适当地整合到基于SAR的洪水后水 extent 映射过程中仍处于探索阶段。为填补这一研究空白，我们提出了一种空间掩码自适应门控网络（SMAGNet），这是一种多模态深度学习模型，利用SAR数据作为洪水后水 extent 映射的主要输入，并通过特征融合整合互补的MSI数据。在C2S-MS洪水数据集上的实验中，SMAGNet在不同MSI数据可用性水平下均优于其他多模态深度学习模型的预测性能。此外，我们发现即使MSI数据完全缺失时，SMAGNet的性能在统计上与仅使用SAR数据训练的U-Net模型相当。这些发现表明，SMAGNet增强了模型对缺失数据的鲁棒性，并提高了多模态深度学习在现实世界洪水管理场景中的应用性。
英文摘要：
Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, during the response stage, when timely and accurate information is important, Synthetic Aperture Radar (SAR) data are primarily employed to produce water extent maps. Recently, leveraging the complementary characteristics of SAR and MSI data through a multimodal approach has emerged as a promising strategy for advancing water extent mapping using deep learning models. This approach is particularly beneficial when timely post-flood observations, acquired during or shortly after the flood peak, are limited, as it enables the use of all available imagery for more accurate post-flood water extent mapping. However, the adaptive integration of partially available MSI data into the SAR-based post-flood water extent mapping process remains underexplored. To bridge this research gap, we propose the Spatially Masked Adaptive Gated Network (SMAGNet), a multimodal deep learning model that utilizes SAR data as the primary input for post-flood water extent mapping and integrates complementary MSI data through feature fusion. In experiments on the C2S-MS Floods dataset, SMAGNet consistently outperformed other multimodal deep learning models in prediction performance across varying levels of MSI data availability. Furthermore, we found that even when MSI data were completely missing, the performance of SMAGNet remained statistically comparable to that of a U-Net model trained solely on SAR data. These findings indicate that SMAGNet enhances the model robustness to missing data as well as the applicability of multimodal deep learning in real-world flood management scenarios.
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