River-Net:面向河道提取的Refined-Lee Kernel深度神经网络模型

李宁 郭志顺 毋琳 赵建辉

李宁, 郭志顺, 毋琳, 等. River-Net:面向河道提取的Refined-Lee Kernel深度神经网络模型[J]. 雷达学报, 2022, 11(3): 324–334. doi: 10.12000/JR21148
引用本文: 李宁, 郭志顺, 毋琳, 等. River-Net:面向河道提取的Refined-Lee Kernel深度神经网络模型[J]. 雷达学报, 2022, 11(3): 324–334. doi: 10.12000/JR21148
LI Ning, GUO Zhishun, WU Lin, et al. River-Net: A novel neural network model for extracting river channel based on Refined-Lee Kernel[J]. Journal of Radars, 2022, 11(3): 324–334. doi: 10.12000/JR21148
Citation: LI Ning, GUO Zhishun, WU Lin, et al. River-Net: A novel neural network model for extracting river channel based on Refined-Lee Kernel[J]. Journal of Radars, 2022, 11(3): 324–334. doi: 10.12000/JR21148

River-Net:面向河道提取的Refined-Lee Kernel深度神经网络模型

doi: 10.12000/JR21148
基金项目: 河南省重大公益项目(201300311300),河南省科技攻关计划项目(212102210101, 212102210093),国家自然科学基金(61871175)
详细信息
    作者简介:

    李 宁(1987–),男,安徽人,于中国科学院电子学研究所获得博士学位,现为河南大学教授,博士生导师,研究方向为多模式合成孔径雷达成像及其应用技术。担任《雷达学报》客座编辑、《电子与信息学报》青年编委等学术兼职

    郭志顺(1995–),男,河南人,河南大学计算机与信息工程学院在读硕士研究生,主要研究方向为合成孔径雷达图像处理及其应用技术

    毋 琳(1978–),女,河南人,河南大学副教授,硕士生导师,主要研究方向为SAR图像处理技术、水环境SAR图像应用

    赵建辉(1980–),男,河南人,河南大学副教授, 硕士生导师,主要研究方向为SAR图像处理

    通讯作者:

    毋琳 henuwl@henu.edu.cn

  • 责任主编:匡纲要 Corresponding Editor: KUANG Gangyao
  • 中图分类号: TN959.1; TP183

River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel

Funds: Major Public Welfare Projects in Henan Province (201300311300), The Plan of Science and Technology of Henan Province (212102210101, 212102210093), The National Natural Science Foundation of China (61871175)
More Information
  • 摘要: 高精度提取合成孔径雷达(SAR)图像中的河流边界,对河流水势监测具有重要意义。以检测郑州7·20暴雨后黄河的健康状况为实施例,该文融合精致Lee滤波思想与卷积操作的滤波特性,提出了基于河道几何特性的优化内部权值卷积核Refined-Lee Kernel,进而提出了一种新型河道提取深度神经网络模型,即River-Net。为验证所提模型的有效性,该文获取了郑州7·20暴雨前后两景欧空局Sentinel-1卫星20 m分辨率干涉宽幅(IW)影像数据,利用暴雨前的影像对模型进行训练,用于提取暴雨后的黄河河道,分析黄河在暴雨后的涨势情况。实验结果表明,相比主流语义分割模型,所提模型能够更精确地在SAR图像中提取河道,对洪水灾害的检测与评估有重要应用价值。

     

  • 图  1  一般卷积与空洞卷积对比图

    Figure  1.  Comparison diagram of convolution and dilated convolution

    图  2  金字塔池化操作示意图

    Figure  2.  The schematic diagram of spatial pyramid pooling

    图  3  精致 Lee 滤波 8 种模板示意图

    Figure  3.  Schematic diagram of refined Lee filter template

    图  4  RLK 模块

    Figure  4.  RLK module

    图  5  River-Net 结构示意图

    Figure  5.  River-Net structure

    图  6  研究区域示意图

    Figure  6.  Region of interest

    图  7  数据集制作示意图

    Figure  7.  Schematic diagram of generating data set

    图  8  不同网络的特征图提取与对比

    Figure  8.  Feature map extraction and comparison of different networks

    图  9  不同网络分割结果对比

    Figure  9.  Comparison with segmentation results of different networks

    图  10  郑州 7·20 暴雨前后部分黄河提取结果

    Figure  10.  Part extraction results of the Yellow River before and after the Zhengzhou 7·20 rainstorm

    表  1  混淆矩阵

    Table  1.   Confusion matrix

    混淆矩阵真实值
    河道背景
    预测值河道TPFP
    背景FNTN
    下载: 导出CSV

    表  2  分割结果评价

    Table  2.   Evaluation of segmentation results

    Algorithms/ModelsPrecision (%)Recall (%)IoU (%)F1-score (%)
    传统方法OTSU93.0584.7481.9988.70
    K-means95.1087.1886.6790.97
    ACM92.0884.0881.9987.90
    深度学习U-Net95.7091.0388.0093.30
    U-Net+RLK96.2293.1891.9294.68
    DeepLab95.4291.1389.4093.23
    PSPNet96.0692.7390.0494.36
    PSPNet+RLK97.1793.2493.3695.16
    River-Net without RLK96.3393.3691.0394.82
    River-Net97.3294.4092.9395.84
    下载: 导出CSV
  • [1] 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
    [2] 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
    [3] MOREIRA A, PRATS-IRAOLA P, YOUNIS M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6–43. doi: 10.1109/MGRS.2013.2248301
    [4] 李宁, 牛世林. 基于局部超分辨重建的高精度SAR图像水域分割方法[J]. 雷达学报, 2020, 9(1): 174–184. doi: 10.12000/JR19096

    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
    [5] 程江华, 高贵, 库锡树, 等. 高分辨率SAR图像道路交叉口检测与识别新方法[J]. 雷达学报, 2012, 1(1): 100–108. doi: 10.3724/SP.J.1300.2012.20024

    CHENG Jianghua, GAO Gui, KU Xishu, et al. A novel method for detecting and identifying road junctions from high resolution SAR images[J]. Journal of Radars, 2012, 1(1): 100–108. doi: 10.3724/SP.J.1300.2012.20024
    [6] LIU Tao, YANG Ziyuan, MARINO A, et al. PolSAR ship detection based on neighborhood polarimetric covariance matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(6): 4874–4887. doi: 10.1109/TGRS.2020.3022181
    [7] LIU Zhongling, LI Fei, LI Ning, et al. A novel region-merging approach for coastline extraction from sentinel-1A IW Mode SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 324–328. doi: 10.1109/LGRS.2015.2510745
    [8] LI Wenyu and GONG Peng. Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery[J]. Remote Sensing of Environment, 2016, 179: 196–209. doi: 10.1016/j.rse.2016.03.031
    [9] 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
    [10] JIANG Liguang, NIELSEN K, DINARDO S, et al. Evaluation of Sentinel-3 SRAL SAR altimetry over Chinese rivers[J]. Remote Sensing of Environment, 2020, 237: 111546. doi: 10.1016/j.rse.2019.111546.0
    [11] 安成锦, 牛照东, 李志军, 等. 典型Otsu算法阈值比较及其SAR图像水域分割性能分析[J]. 电子与信息学报, 2010, 32(9): 2215–2219. doi: 10.3724/SP.J.1146.2009.01426

    AN Chengjin, NIU Zhaodong, LI Zhijun, et al. Otsu threshold comparison and SAR water segmentation result analysis[J]. Journal of Electronics &Information Technology, 2010, 32(9): 2215–2219. doi: 10.3724/SP.J.1146.2009.01426
    [12] BAO Linan, LV Xiaolei, and YAO Jingchuan. Water extraction in SAR Images using features analysis and dual-threshold graph cut model[J]. Remote Sensing, 2021, 13(17): 3465. doi: 10.3390/rs13173465
    [13] 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
    [14] LI Ning, WANG R, LIU Yabo, et al. Robust river boundaries extraction of dammed lakes in mountain areas after Wenchuan Earthquake from high resolution SAR images combining local connectivity and ACM[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 94: 91–101. doi: 10.1016/j.isprsjprs.2014.04.020
    [15] JIA Lu, LI Ming, ZHANG Peng, et al. SAR image change detection based on multiple kernel k-means clustering with local-neighborhood information[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6): 856–860. doi: 10.1109/LGRS.2016.2550666
    [16] SUN Xian, WANG Bing, WANG Zhirui, et al. Research progress on few-shot learning for remote sensing image interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2387–2402. doi: 10.1109/JSTARS.2021.3052869
    [17] LIU Wenjie, ZHANG Wenkai, SUN Xian, et al. HECR-Net: Height-embedding context reassembly network for semantic segmentation in aerial images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 9117–9131. doi: 10.1109/JSTARS.2021.3109439
    [18] LATINI D, DEL FRATE F, PALAZZO F, et al. Coastline extraction from SAR COSMO-SkyMed data using a new neural network algorithm[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 5975–5977. doi: 10.1109/IGARSS.2012.6352247.
    [19] LONG J, SHELHAMER E, and DARRELL T. Fully convolutional networks for semantic segmentation[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 3431–3440. doi: 10.1109/CVPR.2015.7298965.
    [20] PAI M M M, MEHROTRA V, AIYAR S, et al. Automatic segmentation of river and land in SAR images: A deep learning approach[C]. 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering, Sardinia, Italy, 2019: 15–20. doi: 10.1109/AIKE.2019.00011.
    [21] RONNEBERGER O, FISCHER P, and BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]. The 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241. doi: 10.1007/978-3-319-24574-4_28.
    [22] PAI M M M, MEHROTRA V, VERMA U, et al. Improved semantic segmentation of water bodies and land in SAR images using generative adversarial networks[J]. International Journal of Semantic Computing, 2020, 14(1): 55–69. doi: 10.1142/S1793351X20400036
    [23] VERMA U, CHAUHAN A, PAI M M M, et al. DeepRivWidth: Deep learning based semantic segmentation approach for river identification and width measurement in SAR images of Coastal Karnataka[J]. Computers & Geosciences, 2021, 154: 104805. doi: 10.1016/j.cageo.2021.104805.
    [24] 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
    [25] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834–848. doi: 10.1109/TPAMI.2017.2699184
    [26] ZHAO Hengshuang, SHI Jianping, QI Xiaojuan, et al. Pyramid scene parsing network[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 6230–6239. doi: 10.1109/CVPR.2017.660.
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出版历程
  • 收稿日期:  2021-10-09
  • 修回日期:  2021-12-06
  • 网络出版日期:  2021-12-27
  • 刊出日期:  2022-06-28

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