论文标题
专心双流暹罗U-NET，用于多个速率Sentinel-1数据的洪水检测
Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data
论文作者
论文摘要
由于气候和土地利用的变化，近年来洪水等自然灾害一直在增加。及时可靠的洪水检测和地图可以帮助应急和灾难管理。在这项工作中，我们提出了一个使用双阶段SAR收购的洪水检测网络。所提出的分割网络具有一个编码器 - 编码器体系结构，其中有两个用于液化后图像和后滴后图像的暹罗编码器。使用注意力块将网络的特征图融合并增强，以更准确地检测被洪水泛滥的区域。我们提出的网络对公开可用的SEN1FLOOD11基准数据集进行了评估。该网络的表现优于现有的最先进（单位）洪水检测方法6 \％iou。该实验强调，双向SAR数据与有效的网络体系结构的组合比单个临时方法更准确地洪水检测。
Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly available Sen1Flood11 benchmark dataset. The network outperformed the existing state-of-the-art (uni-temporal) flood detection method by 6\% IOU. The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.
