Urban Flood Mapping with Residual Patch Similarity Learning
Urban Flood Mapping with Residual Patch Similarity Learning
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DOI：
10.1145/3356471.3365235
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Bo Peng;Xinyi Liu;Zonglin Meng;Qunying Huang
中科院分区：
文献类型：
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作者：
Bo Peng;Xinyi Liu;Zonglin Meng;Qunying Huang
城市洪水测绘对于抢险救灾、灾后重建和经济损失评估至关重要。在利用多源遥感图像和模式识别算法绘制洪灾范围图方面取得了很大进展。然而，由于以下三个主要原因，高空间分辨率的城市洪水制图仍然是一个主要挑战：（1）非常高分辨率（VHR）光学遥感图像通常具有涉及各种地物（例如，车辆、建筑物、道路和树木），使得传统的分类算法无法捕捉洪水危险区域内相邻像素之间的潜在空间相关性;（2）传统的以手工制作的特征作为输入的洪水制图方法无法充分利用大量可用数据，这需要鲁棒性和可扩展性的算法;（3）由于不同时刻的气象条件不一致，同一地物在VHR光学影像上的像元值可能相差很大，导致经典洪水制图方法的泛化能力较差。为了应对这一挑战，本文提出了一个残差补丁相似性卷积神经网络（ResPSNet），以地图的城市洪水危险区使用双时高分辨率（3米）洪水前和洪水后的多光谱表面反射率卫星图像。此外，遥感特定的数据增强也被开发，以消除由于不同的数据采集条件，这反过来又进一步提高了所提出的模型的性能变化的光照的影响。使用2017年德克萨斯州休斯顿飓风哈维洪水前后的高分辨率图像进行的实验表明，开发的ResPSNet模型，沿着相关的遥感特定数据增强方法，可以稳健地生成高精度的城市地区洪水图（0.9002）、召回率（0.9302）、F1评分（0.9128）和总体准确率（0.9497）。研究揭示了多时相图像融合的高精度图像变化检测，这反过来又可以用于监测自然灾害。
Urban flood mapping is essential for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Much progress has been made to map the extent of flooding with multi-source remote sensing imagery and pattern recognition algorithms. However, urban flood mapping at high spatial resolution remains a major challenge due to three main reasons: (1) the very high resolution (VHR) optical remote sensing imagery often has heterogeneous background involving various ground objects (e.g., vehicles, buildings, roads, and trees), making traditional classification algorithms fail to capture the underlying spatial correlation between neighboring pixels within the flood hazard area; (2) traditional flood mapping methods with handcrafted features as input cannot fully leverage massive available data, which requires robust and scalable algorithms; and (3) due to inconsistent weather conditions at different time of data acquisition, pixels of the same objects in VHR optical imagery could have very different pixel values, leading to the poor generalization capability of classical flood mapping methods. To address this challenge, this paper proposed a residual patch similarity convolutional neural network (ResPSNet) to map urban flood hazard zones using bi-temporal high resolution (3m) pre- and post-flooding multispectral surface reflectance satellite imagery. Besides, remote sensing specific data augmentation was also developed to remove the impact of varying illuminations due to different data acquisition conditions, which in turn further improves the performance of the proposed model. Experiments using the high resolution imagery before and after the 2017 Hurricane Harvey flood in Houston, Texas, showed that the developed ResPSNet model, along with associated remote sensing specific data augmentation method, can robustly produce flood maps over urban areas with high precision (0.9002), recall (0.9302), F1 score (0.9128), and overall accuracy (0.9497). The research sheds light on multitemporal image fusion for high precision image change detection, which in turn can be used for monitoring natural hazards.