基于局部超分辨重建的高精度SAR图像水域分割方法

李宁 牛世林

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
引用本文: 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
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

基于局部超分辨重建的高精度SAR图像水域分割方法

doi: 10.12000/JR19096
基金项目: 国家自然科学基金(U1604145, 61871175, 61601437),河南省高等学校重点科研项目(18B520010, 19A420005),河南省科技攻关计划项目(182102210233, 192102210082),河南省青年人才托举工程(2019HYTP006),河南大学研究生教育创新与质量提升计划项目(SYL18060127)
详细信息
    作者简介:

    李 宁(1987–),男,安徽人,毕业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,研究方向为多模式合成孔径雷达成像及其应用技术。E-mail: lining_nuaa@163.com

    牛世林(1993–),男,河南人,河南大学计算机与信息工程学院硕士研究生,主要研究方向为合成孔径雷达图像处理及其应用技术。E-mail: nsl1993@foxmail.com

    通讯作者:

    李宁 lining_nuaa@163.com

  • 中图分类号: TN959.1; TP183

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

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
  • 摘要: 合成孔径雷达(SAR)图像水域分割在水资源调查、灾害监测等领域具有重要意义。针对中低分辨率星载SAR图像水域提取精度不足的难题,该文融合基于轻量级残差卷积神经网络(CNN)的图像超分辨率重建技术和传统SAR图像水域分割技术的优点,提出了一种基于局部超分辨重建的SAR图像水域分割方法,显著提升了SAR图像水域分割的精度。为了验证上述方法的有效性,该文以南水北调中线工程水源地丹江口水库为应用对象,基于国产高分三号(GF-3)卫星的8 m分辨率标准条带(SS)模式图像和欧空局Sentinel-1卫星20 m分辨率干涉宽幅(IW)模式图像,开展了水域分割的实验验证和精度评估工作。实验结果表明,该文所提方法可在中低分辨率SAR图像中获取更精确的水域分割结果,其水域分割性能较传统方法有大幅提升。

     

  • 图  1  基于局部超分辨重建的SAR图像高精度水域分割方法示意图

    Figure  1.  The structure of local SR based high-precision water extraction method for SAR image

    图  2  局部分块方法示意图

    Figure  2.  Schematic diagram of local block method

    图  3  LRSR网络结构图

    Figure  3.  The structure of the LRSR network

    图  4  精细分割水域边界融合方法

    Figure  4.  Merging method of the refined water boundaries

    图  5  SAR图像3倍超分辨重建结果

    Figure  5.  SR result of SAR images with upscaling factor of 3

    图  6  研究区域

    Figure  6.  Location of study area

    图  7  SAR图像水域分割结果

    Figure  7.  Water extraction results of SAR images

    表  1  LRSR网络卷积与反卷积参数设置

    Table  1.   Convolution and deconvolution parameters of the LRSR

    数据处理层[卷积数目:深度× (${I_i}$×${O_i}$),
    卷积核大小:(${F_i}$×${F_i}$)]
    paddingstride
    特征提取层 [(1×64), (5×5)]01
    特征压缩层 [(64×16), (1×1)]01
    特征映射层 [m×(16×16), (3×3)]11
    特征扩张层 [(1×64), (1×1)]01
    超分辨重建层 [(64×1), (9×9)]4k
    下载: 导出CSV

    表  2  LRSR网络训练参数

    Table  2.   Convolution and deconvolution parameters of the LRSR

    卷积学
    习率
    卷积偏置
    学习率
    反卷积
    学习率
    反卷积偏置
    学习率
    权值衰减最大迭代
    次数
    10–310–410–42 × 10–410–410–7
    下载: 导出CSV

    表  3  LRSR网络MSE损失训练结果

    Table  3.   LRSR training results of MSE loss

    km
    45678
    20.017670.017250.017540.018470.01894
    30.090770.089390.088560.088660.08978
    40.258420.252200.250340.256390.25464
    下载: 导出CSV

    表  4  实验使用的SAR图像详细参数

    Table  4.   Detailed parameters of experimental SAR data

    SAR图像1SAR图像2
    卫星Sentinel-1GF-3
    成像模式干涉宽测绘带(Interferometric Wide-swath, IW)标准条带 (Stand Stripmap, SS)
    成像日期2019年2月8日2017年7月14日
    标称分辨率20 m8 m
    图像尺寸3824×2255像素7428×5221像素
    极化方式VVHH
    下载: 导出CSV

    表  5  对比试验方法

    Table  5.   Comparison test methods

    对比方法方法详情
    方法1水域粗分割(FCM聚类)+ACM精细分割
    方法2水域粗分割(相干斑滤波+FCM聚类+ROI提取)+ACM精细分割
    方法3水域粗分割(相干斑滤波+FCM聚类+ROI提取)+水域边界局部分块法+ACM精细分割
    本文方法水域粗分割(相干斑滤波+FCM聚类+ROI提取)+水域边界局部分块法+图像超分辨重建+ACM精细分割
    下载: 导出CSV

    表  6  定量分析结果

    Table  6.   Results of quantitative analysis

    SAR图像编号对比方法1对比方法2对比方法3本文方法
    虚警率(%)准确率(%)轮廓平均偏
    移像素(个)
    虚警率(%)准确率(%)轮廓平均偏移
    像素(个)
    虚警率(%)准确率(%)轮廓平均偏移
    像素(个)
    虚警率(%)准确率(%)轮廓平均偏
    移像素(个)
    120.4385.232.171410.5991.061.54110.6898.690.79570.01499.650.1543
    29.2790.151.62283.2892.431.36520.9198.830.74680.03499.720.1403
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-06
  • 修回日期:  2020-02-02
  • 网络出版日期:  2020-02-28

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