Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images

被引:148
|
作者
Amitrano, Donato [1 ]
Di Martino, Gerardo [1 ]
Iodice, Antonio [1 ]
Riccio, Daniele [1 ]
Ruello, Giuseppe [1 ]
机构
[1] Univ Napoli Federico II, Dept Elect Engn & Informat Technol, I-80125 Naples, Italy
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2018年 / 56卷 / 06期
关键词
Classification; co-occurrence texture; flooding; fuzzy systems; synthetic aperture radar (SAR); WATER INDEX NDWI; SEMIARID REGIONS; RESERVOIRS; SYSTEM; EXTENT;
D O I
10.1109/TGRS.2018.2797536
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
We present a new methodology for rapid flood mapping exploiting Sentinel-1 synthetic aperture radar data. In particular, we propose the usage of ground range detected (GRD) images, i.e., preprocessed products made available by the European Space Agency, which can be quickly treated for information extraction through simple and poorly demanding algorithms. The proposed framework is based on two processing levels providing event maps with increasing resolution. The first level exploits classic co-occurrence texture measures combined with amplitude information in a fuzzy classification system avoiding the critical step of thresholding. The second level consists of a change-detection approach applied to the full resolution GRD product. The discussion is supported by several experiments demonstrating the potentiality of the proposed methodology, which is particularly oriented toward the end-user community.
引用
收藏
页码:3290 / 3299
页数:10
相关论文
共 50 条
  • [1] A NOVEL TOOL FOR UNSUPERVISED FLOOD MAPPING USING SENTINEL-1 IMAGES
    Amitrano, D.
    Di Martino, G.
    Iodice, A.
    Riccio, D.
    Ruello, G.
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 4909 - 4912
  • [2] Use of Sentinel-1 GRD SAR Images to Delineate Flood Extent in Pakistan
    Zhang, Meimei
    Chen, Fang
    Liang, Dong
    Tian, Bangsen
    Yang, Aqiang
    SUSTAINABILITY, 2020, 12 (14) : 1 - 19
  • [3] Performance of Random Forest Classifier for Flood Mapping Using Sentinel-1 SAR Images
    Chu, Yongjae
    Lee, Hoonyol
    KOREAN JOURNAL OF REMOTE SENSING, 2022, 38 (04) : 375 - 386
  • [4] An exploratory study of Sentinel-1 SAR for rapid urban flood mapping on Google Earth Engine
    Islam, Md Tazmul
    Meng, Qingmin
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2022, 113
  • [5] Unsupervised mapping of rice paddy fields and their inundation patterns using Sentinel-1 SAR images and GIS
    McGiven, Lauren E.
    Mueller, Marc F.
    EUROPEAN JOURNAL OF REMOTE SENSING, 2025, 58 (01)
  • [6] GEE4FLOOD: rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth Engine cloud platform
    Vanama, Venkata Sai Krishna
    Mandal, Dipankar
    Rao, Yalamanchili Subrahmanyeswara
    JOURNAL OF APPLIED REMOTE SENSING, 2020, 14 (03)
  • [7] Combining Time-Series Variation Modeling and Fuzzy Spatiotemporal Feature Fusion: A Novel Approach for Unsupervised Flood Mapping Using Dual-Polarized Sentinel-1 SAR Images
    Li, Congyu
    Liu, Jiaqi
    Liu, Xinxin
    Kang, Xudong
    Li, Shutao
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61
  • [8] Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
    Uddin, Kabir
    Matin, Mir A.
    Meyer, Franz J.
    REMOTE SENSING, 2019, 11 (13)
  • [9] Inundation mapping of Kerala flood event in 2018 using ALOS-2 and temporal Sentinel-1 SAR images
    Vanama, V. S. K.
    Musthafa, Mohamed
    Khati, Unmesh
    Gowtham, R.
    Singh, Gulab
    Rao, Y. S.
    CURRENT SCIENCE, 2021, 120 (05): : 915 - 925
  • [10] Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and-2 Imagery
    Landuyt, Lisa
    Verhoest, Niko E. C.
    Van Coillie, Frieke M. B.
    REMOTE SENSING, 2020, 12 (21) : 1 - 20