Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data
Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data
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DOI：
10.1109/jstars.2021.3051873
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发表时间：
2021-01-01
影响因子：
5.5
通讯作者：
Gebrehiwot, Asmamaw A.
中科院分区：
文献类型：
--
作者：
Hashemi-Beni, Leila;Gebrehiwot, Asmamaw A.
洪水频繁发生，造成生命损失，并对基础设施和环境造成广泛破坏。准确和及时地绘制洪水范围图以确定损失是救灾活动的关键和必要条件。最近，基于深度学习的方法，包括卷积神经网络（CNN），在洪水范围映射方面显示出了有希望的结果。然而，这些方法不能提取洪水下植被冠层使用光学图像。本文试图通过引入一种集成的CNN和区域生长（RG）方法来解决这个问题，用于绘制可见和地下植被淹没区。基于CNN的分类器用于从光学图像中提取淹没区域，而RG方法用于估计使用数字高程模型从图像中不可见的植被下的洪水程度。数据增强技术被应用于训练基于CNN的分类器，以改善分类结果。结果表明，数据增强可以提高图像分类的准确性，所提出的综合方法有效地检测洪水在可见光和植被覆盖的地区，这是必不可少的，以支持有效的洪水应急响应和恢复活动。
Flooding occurs frequently and causes loss of lives, and extensive damages to infrastructure and the environment. Accurate and timely mapping of flood extent to ascertain damages is critical and essential for relief activities. Recently, deep-learning-based approaches, including convolutional neural network (CNN) has shown promising results for flood extent mapping. However, these methods cannot extract floods underneath vegetation canopy using the optical imagery. This article attempts to address this problem by introducing an integrated CNN and region growing (RG) method for the mapping of both visible and underneath vegetation flooded areas. The CNN-based classifier is used to extract flooded areas from the optical images, whereas, the RG method is applied to estimate the extent of floods underneath vegetation that are not visible from imagery using the digital elevation model. A data augmentation technique is applied for training the CNN-based classifier to improve the classification results. The results show that the data augmentation can enhance the accuracy of image classification and the proposed integrated method efficiently detects floods in both the visible and the areas covered by vegetation, which is essential to supporting effective flood emergency response and recovery activities.