Sentinel-1 SAR在洪水范围提取与极化分析中的应用研究

在洪水灾情监测中,快速准确的获取淹没区域和洪灾面积,对防汛救灾和灾后重建工作具有重要价值.本文以2017年美国圣路易斯洪水为例,基于Sentinel-1 SAR数据,利用变化检测和阈值相结合的方法实现大范围洪水淹没提取,将VV/VH极化数据分别与从同期Sentinel-2光学影像中获取的洪水淹没范围进行比较,评定极化方...

Application Research of Sentinel-1 SAR in Flood Range Extraction and Polarization Analysis

In flood disaster monitoring, fast and accurate detection of inundated area and flooded disaster region is of great value for flood control and post-disaster reconstruction work. This paper takes the 2017 Saint Louis flood in the United States as an example. Based on Sentinel-1 SAR data, the method of combining change detection and threshold was used to achieve large-scale flood inundation extraction. Firstly, the SAR data were pre-processed with sigma radiation calibration and Refined Lee filtering, which were effective in improving the contrast of land and water bodies, as well as attenuating the coherent speckle noise. Secondly, the difference image between the reference image and flooded image was defined by change detection methodology and the image histogram was divided by the quantile threshold method to extract the submerge area. Finally, image post-processing was performed on the thresholded results to reduce misclassification. Areas not close to the water surface and whose slope was higher than 3 degrees were defined as non-flood region for exclusion using the digital elevation model. Then, the small particle noise and holes were removed by morphological filtering to achieve large-scale flood inundation extraction. The boundary information was retained while keeping the original size of the flood category unchanged. Heavy rainfall was the main cause of the 2017 extensive flooding in Saint Louis. The low-lying northern river bend area was the most severely affected, inundated for up to two months while the main city suffered less damage due to its high terrain and timely flood protection. Until now, there have been few studies on the effectiveness of different synthetic aperture radar data polarization modes in relation to flood detection. The Sentinel-1 VV/VH polarization data were compared with the flood inundation extraction range obtained from the Sentinel-2 optical image during the same period. Then, the flood detection applicability of the polarization mode was evaluated based on the comparison results. The scattering response characteristics in the multi-polarization patterns were analyzed by plotting the back-scattering cross-sectional lines for different periods of each polarization. The results show that both Sentinel-1 VV and VH polarization data can identify flood with a high accuracy of over 82%. Compared with VH polarization mode, VV polarization mode has fewer false positives. In the same region, the scattering degree of Sentinel-1 VV polarization signal was 28% smaller than that of VH, showing more sensitive information from the flood. Therefore, Sentinel-1 VV polarization mode is more suitable for monitoring the inundation range of flood disaster.