Volume 9 Issue 1
Feb.  2020
Turn off MathJax
Article Contents
LI Ning and NIU Shilin. High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9(1): 174–184. doi: 10.12000/JR19096
Citation: LI Ning and NIU Shilin. High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9(1): 174–184. doi: 10.12000/JR19096

High-precision Water Segmentation from Synthetic Aperture Radar Images Based on Local Super-resolution Restoration Technology

doi: 10.12000/JR19096
Funds:  The National Natural Science Foundation of China (U1604145, 61871175, 61601437), The College Key Research Project of Henan Province (18B520010, 19A420005), The Plan of Science and Technology of Henan Province (182102210233, 192102210082), The Youth Talent Lifting Project of Henan Province (2019HYTP006), The Graduate Education Innovation and Quality Improvement Program of henan University (SYL18060127)
More Information
  • Corresponding author: LI Ning, lining_nuaa@163.com
  • Received Date: 2019-11-06
  • Rev Recd Date: 2020-02-02
  • Available Online: 2020-02-27
  • Publish Date: 2020-02-28
  • The extraction of water from Synthetic Aperture Radar (SAR) images is of great significance in water resources investigation and monitoring disasters. To deal with the problems of the insufficient accuracy of water boundaries extracted from middle-low resolution SAR images. This paper proposes a high-precision water boundaries extraction method based on a local super-resolution restoration technology that combines the advantages of the super-resolution restoration technology based on the lightweight residual Convolutional Neural Network (CNN) and the traditional SAR images water extraction methods. The proposed method can significantly improve the accuracy of water segmentation results by using SAR images. To verify the effectiveness of the proposed method, as a study area, we selected the Danjiangkou Reservoir, the water source of the middle route of a south-to-north water diversion project. Further, we conducted experiments on the multi-mode SAR dataset and evaluated its accuracy. This dataset included one Standard Strip-map (SS) mode image obtained by the Chinese GaoFen-3 (GF-3) satellite with a resolution of 8 m and one Interferometric Wide-swath (IW) mode SAR image obtained by Sentinel-1 satellite with a resolution of 20 m. The experimental results showed that the water segmentation results from the middle–low resolution SAR images of the proposed method were more precise, and the overall water segmentation performance was superior to that of the traditional methods.

     

  • loading
  • [1]
    牛世林, 郭拯危, 李宁, 等. 星载SAR水域分割研究进展与趋势分析[J]. 聊城大学学报: 自然科学版, 2018, 118(2): 75–89.

    NIU Shilin, GUO Zhengwei, LI Ning, et al. Research progress and trend analysis of water extraction by spaceborne SAR[J]. Journal of Liaocheng University:Natural Science Edition, 2018, 118(2): 75–89.
    [2]
    LI Ning, NIU Shilin, GUO Zhengwei, et al. Dynamic waterline mapping of inland great lakes using time-series SAR data from GF-3 and S-1A satellites: A case study of DJK reservoir, China[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(11): 4297–4314. doi: 10.1109/JSTARS.2019.2952902
    [3]
    邓云凯, 赵凤军, 王宇. 星载SAR技术的发展趋势及应用浅析[J]. 雷达学报, 2012, 1(1): 1–10. doi: 10.3724/SP.J.1300.2012.20015

    DENG Yunkai, ZHAO Fengjun, and WANG Yu. Brief analysis on the development and application of spaceborne SAR[J]. Journal of Radars, 2012, 1(1): 1–10. doi: 10.3724/SP.J.1300.2012.20015
    [4]
    GUO Yaru and ZHANG Jixian. A new 2D OTSU for water extraction from SAR image[J]. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, XLII-2/W7: 733–736. doi: 10.5194/isprs-archives-XLII-2-W7-733-2017
    [5]
    LI Ning, WANG R, DENG Yunkai, et al. Waterline mapping and change detection of Tangjiashan dammed lake after Wenchuan earthquake from multitemporal high-resolution airborne SAR imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3200–3209. doi: 10.1109/JSTARS.2014.2345417
    [6]
    冷英, 刘忠玲, 张衡, 等. 一种改进的ACM算法及其在鄱阳湖水域监测中的应用[J]. 电子与信息学报, 2017, 39(5): 1064–1070. doi: 10.11999/JEIT160870

    LENG Ying, LIU Zhongling, ZHANG Heng, et al. Improved ACM algorithm for Poyang lake monitoring[J]. Journal of Electronics &Information Technology, 2017, 39(5): 1064–1070. doi: 10.11999/JEIT160870
    [7]
    冷英, 李宁. 一种改进的变化检测方法及其在洪水监测中的应用[J]. 雷达学报, 2017, 6(2): 204–212. doi: 10.12000/JR16139

    LENG Ying and LI Ning. Improved change detection method for flood monitoring[J]. Journal of Radars, 2017, 6(2): 204–212. doi: 10.12000/JR16139
    [8]
    张金松, 邢孟道, 孙光才. 一种基于密集深度分离卷积的SAR图像水域分割算法[J]. 雷达学报, 2019, 8(3): 400–412. doi: 10.12000/JR19008

    ZHANG Jinsong, XING Mengdao, and SUN Guangcai. A water segmentation algorithm for SAR image based on dense depthwise separable convolution[J]. Journal of Radars, 2019, 8(3): 400–412. doi: 10.12000/JR19008
    [9]
    SZILAGYI L, BENYÓ Z, SZILAGYI S M, et al. MR brain image segmentation using an enhanced fuzzy C-means algorithm[C]. The 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico, 2015, 1: 724–726. doi: 10.1109/IEMBS.2003.1279866.
    [10]
    CHEN Jiaqi, WANG Qingwei, WANG Jian, et al. Change detection of water index in danjiangkou reservoir using mixed log-normal distribution based active contour model[J]. IEEE Access, 2019, 7: 95430–95442. doi: 10.1109/ACCESS.2019.2929178
    [11]
    DONG Chao, LOY C C, HE Kaiming, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 38(2): 295–307. doi: 10.1109/TPAMI.2015.2439281
    [12]
    DONG Chao, LOY C C, and TANG Xiaoou. Accelerating the super-resolution convolutional neural network[C]. The 14th European Conference on Computer Vision (ECCV’16), Amsterdam, The Netherlands, 2016: 391–407. doi: 10.1007/978-3-319-46475-6_25.
    [13]
    KIM J, LEE J K, and LEE K M. Accurate image super-resolution using very deep convolutional networks[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), Las Vegas, USA, 2016: 1646–1654. doi: 10.1109/CVPR.2016.182.
    [14]
    LEDIG C, THEIS L, HUSZÁR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 105–114. doi: 10.1109/CVPR.2017.19.
    [15]
    LIM B, SON S H, KIM H W, et al. Enhanced deep residual networks for single image super-resolution[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, USA, 2017: 1132–1140. doi: 10.1109/CVPRW.2017.151.
    [16]
    FENG Hongxiao, HOU Biao, and GONG Maoguo. SAR Image despeckling based on local homogeneous-region segmentation by using pixel-relativity measurement[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(7): 2724–2737. doi: 10.1109/TGRS.2011.2107915
    [17]
    ZEILER M D and FERGUS R. Visualizing and understanding convolutional networks[C]. The 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland, 2014: 818–833. doi: 10.1007/978-3-319-10590-1_53.
    [18]
    郭玥秀, 杨伟, 刘琦, 等. 残差网络研究综述[J/OL]. 计算机应用研究: 1–8. http://kreader.cnki.net/Kreader/CatalogViewPage.aspx?dbCode=CJFQ&filename=JSYJ20190606001&tablename=CAPJLAST&compose=&first=1&uid=, 2019.

    GUO Yuexiu, YANG Wei, LIU Qi, et al. Survey of residual network[J/OL]. Application Research of Computers: 1–8. http://kreader.cnki.net/Kreader/CatalogViewPage.aspx?dbCode=CJFQ&filename=JSYJ20190606001&tablename=CAPJLAST&compose=&first=1&uid=, 2019.
    [19]
    HE Kaiming, ZHANG Xingyu, REN Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015: 1026–1034. doi: 10.1109/ICCV.2015.123.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Article views(3860) PDF downloads(333) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint