地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (6): 1063-1070.doi: 10.12082/dqxxkx.2021.200717

• 遥感科学与应用技术 • 上一篇    下一篇

Sentinel-1 SAR在洪水范围提取与极化分析中的应用研究

陈赛楠1(), 蒋弥2,*()   

  1. 1.河海大学地球科学与工程学院,南京211100
    2.中山大学测绘科学与技术学院,广州 519000
  • 收稿日期:2020-11-28 修回日期:2021-03-12 出版日期:2021-06-25 发布日期:2021-08-25
  • 通讯作者: 蒋弥
  • 作者简介:陈赛楠(1996— ),女,江苏南通人,硕士生,主要研究方向为合成孔径雷达变化检测及洪水应用。 E-mail: DianeChenhhu@163.com
  • 基金资助:
    国家重点研发计划项目(2018YFC0407900)

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

CHEN Sainan1(), JIANG Mi2,*()   

  1. 1. School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
    2. Cchool of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou 519000, China
  • Received:2020-11-28 Revised:2021-03-12 Online:2021-06-25 Published:2021-08-25
  • Contact: JIANG Mi
  • Supported by:
    National Key Research and Development Program of China(2018YFC0407900)

摘要:

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,评定极化方式的洪水适用性优劣程度。不同的SAR极化数据对洪水监测的适用性不同,通过绘制各极化不同时期的后向散射横断面线来分析多极化中的散射响应特征。研究表明:Sentinel-1 VV/VH极化数据均能以超过82%的高精度识别出洪水,VV极化洪水提取时产生的误判更少;在同样的区域,相较于VH,Sentinel-1 VV极化信号的散射程度小了约28%,在洪水中的信息敏感,更适用于洪水灾害的淹没范围监测。

关键词: 洪水监测, Sentinel-1, SAR, 美国圣路易斯洪水, 极化分析, 变化检测, 淹没范围, Sentinel-2

Abstract:

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.

Key words: flood monitoring, Sentinel-1, SAR, Floods in Saint Louis,USA, polarization analysis, change detection, flood submerge area, Sentinel-2