Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data
Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data
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DOI：
10.3390/rs15020532
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发表时间：
2023-01
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影响因子：
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通讯作者：
H. Gokon;Fuyuki Endo;S. Koshimura
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文献类型：
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作者：
H. Gokon;Fuyuki Endo;S. Koshimura
当大规模洪水灾害发生时，重要的是在短时间内确定洪水区域，以便事后有效地支持受灾地区。合成孔径雷达（SAR）是一种很有前途的洪水探测技术。已经提出了一些变化检测方法，以检测洪水与事件前和事件后的SAR数据的地区。然而，由于微波散射的复杂性，在建筑物密集的地区仍然很难检测到洪水区域。为了解决这个问题，在本文中，我们提出的想法，分析的本地变化，在事件前和事件后的SAR数据，以及更大的范围内的变化，这可能会提高精度检测洪水在建成区。因此，我们的目的是评估多尺度SAR分析的有效性，洪水检测在建成区使用ALOS-2/PALSAR-2数据。首先，通过计算具有几种大小的核的标准差图像、差图像和相关系数图像来确定几个特征。然后，对相关系数图像进行小尺度和大尺度的分割，并利用每个分割的特征计算解释变量。最后，通过比较小尺度方法和多尺度方法，测试了机器学习模型在建成区的洪水检测性能。使用十重交叉验证来验证模型，表明AdaBoost模型提供了最高的准确性，其将F1得分从小尺度分析中的0.89提高到多尺度分析中的0.98。本文的主要贡献是，从我们的研究结果可以推断，多尺度分析在定量检测建成区洪水方面表现出更好的性能。
When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas.