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Inundation monitoring of immovable cultural relics with time-series SAR images

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：wufan@aircas.ac.cn
  • Introduction：吴樊，1976年生，男，副研究员，研究方向为SAR图像处理与信息提取。E-mail： wufan@aircas.ac.cn
WU Fan1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LI Juanjuan12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHANG Bo1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：wangchao@radi.ac.cn
  • Introduction：王超，1963年生，男，研究员，研究方向为SAR大数据智能处理与方法。E-mail： wangchao@radi.ac.cn
WANG Chao12*,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHANG Hong1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

CHEN Fulong1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LI Lu12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

XU Lu1

Resümee

Immovable cultural relics are crucial material in cultural heritage. In recent years, meteorological and hydrological disasters, such as flood storms and other disasters, have been a significant threat to immovable cultural relics due to global climate change, torrential weather, and other extreme weather. Thus, extensive mapping and dynamic monitoring of water bodies timely are essential. Synthetic Aperture Radar (SAR) has the advantages of all-day, all-weather, and large-scale periodic earth observation and plays a key role in the application of large-scale water body monitoring.In this paper, a framework for monitoring inundation and risk of immovable cultural heritage based on residual U-Net is proposed by using time series SAR images. First, on the basis of the bimodal threshold segmentation method and combined with expert knowledge, water sample generation was carried out to improve the efficiency of the sample production. Second, the U-shaped convolutional network was established by introducing the residual module, which combined the characteristics of the residual structure and U-Net to alleviate the gradient dispersion and disappearance during the value updating. By accumulating and jumping links between convolutional layers, more feature information of objects is retained to achieve rapid and high-precision semantic segmentation of water bodies. Finally, by superimposing the water extraction results with the area of immovable cultural relics, the waterlogging status of immovable cultural relics can be monitored.Poyang Lake and the relic of Changyi Beidang in Nanchang were selected for the experiments. A total of 21 Sentinel-1 images covering Poyang Lake at different dates were obtained for water body extraction. The results were analyzed and evaluated by combining corresponding Sentinel-2 optical images. The experimental results show that the overall accuracy of the proposed method in the Poyang Lake experimental area is greater than 95%, and the proposed method outperforms Fully Convolutional Networks (FCN) and U-Net methods.Spatial superposition analysis was conducted combining the site of relic of Changyi with the monitoring results of water bodies from SAR images in long-term series. The experimental results unveil that the method proposed in this paper can extract water bodies and has great potential for inundation and risk monitoring of immovable cultural relics.

Schlüsselwort

remote sensing;synthetic aperture radar;immovable cultural relics;water extraction;deep learning;flooding monitoring

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