| 2020年主汛期长江流域暴雨特征及成因分析 |
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| 引用本文： | 眭海刚, 赵博飞, 徐川, 周明婷, 杜卓童, 刘俊怡. 多模态序列遥感影像的洪涝灾害应急信息快速提取[J]. 武汉大学学报 ( 信息科学版), 2021, 46(10): 1441-1449. DOI: 10.13203/j.whugis20210465 |
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| 作者姓名： | 眭海刚 赵博飞 徐川 周明婷 杜卓童 刘俊怡 |
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| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室，湖北 武汉，430079;2.湖北工业大学计算机学院，湖北 武汉，430068 |
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| 基金项目： | 国家自然科学基金（41771457）；国家重点研发计划（2016YFB0502600） |
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| 摘 要： | 遥感对地观测技术具有响应快、观测范围大、表达地表信息客观等特点，是监测洪涝灾害的有效手段之一。洪涝灾害发生时常常伴随云雨天气，灾害前后获取的时间序列数据来源多样，利用多模态多时相遥感影像对洪涝灾害进行一体化监测是大势所趋。然而，不同传感器类型的数据处理平台不同、处理流程不一，多源数据协同处理链路长、智能化水平低导致时效性难以满足应急响应的需求。提出了一种多模态序列遥感影像一体化配准与洪涝灾害自动变化监测方法，利用深度特征和语义信息实现灾前光学影像和灾后合成孔径雷达（synthetic aperture radar，SAR）影像的自动、高精度配准，基于先验基础地理信息和时间序列遥感影像实现洪水变化监测和灾损信息提取。所提方法在2020年7月中国安徽洪涝灾害和2021年7月中国河南洪涝灾害监测中得到了有效验证，能够实现小时级的灾害应急信息提取。
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| 关 键 词： | 洪涝灾害 多模态遥感影像 影像配准 变化检测 灾损提取 |
| 收稿时间： | 2021-09-18 |
Satellite Imaging Reveals Increased Proportion of Population Exposed to Floods |
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| SUI Haigang,ZHAO Bofei,XU Chuan,ZHOU Mingting,DU Zhuotong,LIU Junyi. Satellite Imaging Reveals Increased Proportion of Population Exposed to Floods[J]. Geomatics and Information Science of Wuhan University, 2021, 596(10): 1441-1449. DOI: 10.13203/j.whugis20210465 |
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| Authors: | SUI Haigang ZHAO Bofei XU Chuan ZHOU Mingting DU Zhuotong LIU Junyi |
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| Affiliation: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2.School of Computer Science, Hubei University of Technology, Wuhan 430068, China |
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| Abstract: | Objectives Flood disasters are often accompanied by cloud and rain. Time series data obtained before and after the disaster come from various sources, how to integrate multi-modal and multi-temporal remote sensing images to monitoring flood is the general trend. Methods We propose a flood monitoring method using multi-modal sequence remote sensing image integration registration and automatic change detection. The depth features and semantic information can be used to realize automatic and high-precision registration of optical and SAR(synthetic aperture radar) images before and after disasters. Based on geographic information and multi-time series remote sensing images, we realize flood change monitoring and submergence information extraction. Results The method has been effectively verified by flood in Anhui, China in July 2020 and in Henan, China in July 2021, and also can achieve faster acquisition of post-disaster damage information. Conclusions This work solves the problem of emergency flood monitoring in the case of rainy weather, and puts forward several suggestions on the application of remote sen-sing technology to disaster events in our country. |
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| Keywords: | flood disaster multi-modal remote sensing images image registration change detection submerged information extraction |
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