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一种超分辨SAR图像水域分割算法及其应用

陈嘉琪 刘祥梅 李宁 张燕

陈嘉琪, 刘祥梅, 李宁, 张燕. 一种超分辨SAR图像水域分割算法及其应用[J]. 电子与信息学报, 2021, 43(3): 700-707. doi: 10.11999/JEIT200366
引用本文: 陈嘉琪, 刘祥梅, 李宁, 张燕. 一种超分辨SAR图像水域分割算法及其应用[J]. 电子与信息学报, 2021, 43(3): 700-707. doi: 10.11999/JEIT200366
Jiaqi CHEN, Xiangmei LIU, Ning LI, Yan ZHANG. A High-precision Water Segmentation Algorithm for SAR Image and its Application[J]. Journal of Electronics and Information Technology, 2021, 43(3): 700-707. doi: 10.11999/JEIT200366
Citation: Jiaqi CHEN, Xiangmei LIU, Ning LI, Yan ZHANG. A High-precision Water Segmentation Algorithm for SAR Image and its Application[J]. Journal of Electronics and Information Technology, 2021, 43(3): 700-707. doi: 10.11999/JEIT200366

一种超分辨SAR图像水域分割算法及其应用

doi: 10.11999/JEIT200366
基金项目: 国家自然科学基金(61771183, 61601437),中央高校基础研究基金(2016B07114),河南省科技攻关计划项目(192102210082),河南省青年人才托举工程(2019HYTP006),中国博士后科学基金(2013M541035)
详细信息
    作者简介:

    陈嘉琪:男,1984年生,副教授,硕士生导师,研究方向为雷达信号处理及应用,计算电磁学

    刘祥梅:男,1997年生,硕士生,研究方向为SAR图像处理

    李宁:男,1987年生,教授,博士生导师,研究方向为SAR信号与图像处理

    张燕:女,1994年生,硕士生,研究方向为遥感图像超分辨率重建

    通讯作者:

    陈嘉琪 cjq19840130@163.com

  • 中图分类号: TN959.1

A High-precision Water Segmentation Algorithm for SAR Image and its Application

Funds: The National Natural Science Foundation of China (61771183, 61601437), The Fundamental Research Funds for the Central University (2016B07114), The Plan of Science and Technology of Henan Province (192102210082), The Youth Talent Lifting Project of Henan Province (2019HYTP006), China Postdoctoral Science Foundation (2013M541035)
  • 摘要: 合成孔径雷达(SAR)图像水域分割在湖泊、河流等陆地水文监测领域有重要的研究意义。由于SAR图像分辨率不足所导致的陆地与水域边界模糊, 会影响水域分割精度。该文以中国青藏高原地区的多庆错湖为研究对象,使用Sentinel-1A SAR图像数据,综合运用深度残差模型、通道注意力与亚像素卷积,提出一种基于亚像素卷积的增强型通道注意力深度残差超分辨网络,对滤波后的SAR图像进行重建、水域轮廓提取与精度分析。通过比较不同超分辨算法下的重建结果及水域轮廓提取精度,该文算法在重建效果与提取精度上都较传统方法有明显提升,并具有很好的鲁棒性。
  • 图  1  算法整体流程图

    图  2  通道注意力机制示意图

    图  3  IEDSR网络损失值变化曲线

    图  4  3种超分辨算法对多庆错湖影像处理结果对比

    图  5  多庆错湖轮廓提取结果

    图  6  2015年9月至2016年8月应用IEDSR水域提取结果

    表  1  8组多庆错湖影像重建质量评估(PSNR(dB)/SSIM)

    日期BicubicSRCNN本文IEDSR
    2015090227.41/0.7628.46/0.8228.22/0.85
    2015101624.64/0.6825.38/0.7526.39/0.85
    2015112527.05/0.7828.21/0.8428.28/0.86
    2016040125.86/0.7826.90/0.8427.43/0.89
    2016041727.09/0.8328.46/0.8827.93/0.89
    2016061224.12/0.7624.99/0.8324.90/0.86
    2016073027.71/0.7928.94/0.8529.04/0.88
    2016082328.61/0.8129.55/0.8629.88/0.88
    平均值26.56/0.7727.61/0.8327.76/0.87
    下载: 导出CSV

    表  2  不同时期轮廓提取精度比较(OM(%)/COM(%)/Dis像素)

    日期原图BicubicSRCNN本文IEDSR
    201509020.39/0.29/0.730.26/0.13/0.440.22/0.11/0.350.14/0.13/0.29
    201510160.26/1.14/1.030.22/0.56/0.610.18/0.64/0.600.05/0.78/0.60
    201511250.12/2.83/2.320.17/1.14/1.030.08/1.24/1.010.02/1.24/0.97
    201604010.12/2.74/2.250.19/0.88/0.830.08/0.93/0.770.01/0.64/0.51
    201604170.28/10.57/2.960.50/3.84/1.800.43/3.24/1.500.02/2.44/0.99
    201606120.62/12.93/2.700.61/4.41/0.890.48/4.43/0.860.04/3.15/0.55
    201607300.23/0.54/0.770.13/0.28/0.410.05/0.51/0.570.01/0.28/0.30
    201608230.16/0.30/0.520.01/0.11/0.220.09/0.05/0.150.05/0.08/0.10
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-08
  • 修回日期:  2020-12-05
  • 网络出版日期:  2020-12-17
  • 刊出日期:  2021-03-22

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