A Novel Flood Monitoring Method Using Temporal Information and Statistical Characteristics in SAR Images

Le résumé fourni par la source

Synthetic Aperture Radar (SAR) has the ability of all-weather and all-day observation, which is suitable for flood monitoring. However, there still has some challenges in flood monitoring using SAR images, such as high-quality prior knowledge, accumulated errors in time series analysis, and the impact of other land cover changes. To solve these problems, in this paper, a novel unsupervised flood monitoring method using temporal information and statistical characteristics of SAR images is proposed. Firstly, the temporal-spatial-polarization (TSP) dataset is constructed from different SAR data. Furthermore, improved K-means clustering is proposed to fit these constructed datasets to mitigate the error accumulation. Finally, considering the statistical characteristics of SAR data, the Bray-Curtis distance is applied to optimize improved K-means. To verify the effectiveness of the proposed method, the latest flood event in Jingpo Lake in China with temporal Sentinel-1 data is used. The experimental results demonstrate that our method has superior performance in detecting flood regions, with OA and Kappa of up to 97.64% and 0.86.

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