Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets
Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets
复制标题
DOI：
10.3390/rs13050905
复制
发表时间：
2021-03-01
期刊：
影响因子：
5
通讯作者：
Liu, Renyi
中科院分区：
文献类型：
--
作者：
Wu, Chuyi;Zhang, Feng;Liu, Renyi
建筑物的受损状况是灾后救援和重建规划的重要依据，也是难以检测和判断其受损程度的重要依据。现有的研究大多集中在二元分类上，模型的注意力被分散了。在这项研究中，我们提出了一个暹罗神经网络，可以定位和分类受损建筑物在同一时间。这个网络的主要部分是使用不同主干的各种注意力U网。注意力机制使网络更加关注有效的特征和渠道，从而减少无用特征的影响。我们使用xBD数据集训练它们，这是一个用于推进建筑物损坏评估的大规模数据集，并比较它们的结果平衡F（F1）得分。分数表明，具有注意力机制的SEresNeXt的性能在单个模型中表现最好，F1分数达到0.787。为了提高准确性，我们融合了结果，得到了最好的整体F1得分0.792。为了验证模型的可移植性和鲁棒性，我们选择了最近两次灾难的Maxar Open Data Program数据集来研究模型的性能。通过直观比较，结果表明，我们的模型是强大的和可移植的。
The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance among single models, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.