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基于能量最小化的星载SAR图像建筑物分割方法

周则明 孟勇 黄思训 胡宝鹏

周则明, 孟勇, 黄思训, 胡宝鹏. 基于能量最小化的星载SAR图像建筑物分割方法. 自动化学报, 2016, 42(2): 279-289. doi: 10.16383/j.aas.2016.c150460
引用本文: 周则明, 孟勇, 黄思训, 胡宝鹏. 基于能量最小化的星载SAR图像建筑物分割方法. 自动化学报, 2016, 42(2): 279-289. doi: 10.16383/j.aas.2016.c150460
ZHOU Ze-Ming, MENG Yong, HUANG Si-Xun, HU Bao-Peng. Building Segmentation of Spaceborne SAR Images Based on Energy Minimization. ACTA AUTOMATICA SINICA, 2016, 42(2): 279-289. doi: 10.16383/j.aas.2016.c150460
Citation: ZHOU Ze-Ming, MENG Yong, HUANG Si-Xun, HU Bao-Peng. Building Segmentation of Spaceborne SAR Images Based on Energy Minimization. ACTA AUTOMATICA SINICA, 2016, 42(2): 279-289. doi: 10.16383/j.aas.2016.c150460

基于能量最小化的星载SAR图像建筑物分割方法

doi: 10.16383/j.aas.2016.c150460
基金项目: 

国家自然科学基金 41275029

国家自然科学基金 41175025

国家自然科学基金 61473310

国家自然科学基金 41174164

国家自然科学基金 41305138

公益性行业 (气象) 科研专项 GYHY201306068

详细信息
    作者简介:

    周则明  解放军理工大学气象海洋学院教授.主要研究方向为计算机视觉, 医学图像处理和遥感影像分析.E-mail:zhou_zeming@yahoo.com

    黄思训  解放军理工大学气象海洋学院教授.主要研究方向为流体力学与大气科学中的非线性问题的理论.E-mail:huangsxp@163.com

    胡宝鹏  解放军理工大学气象海洋学院硕士研究生, 96427部队工程师.主要研究方向为遥感图像处理和分析.E-mail:baopeng_hu@163.com

    通讯作者:

    孟勇  解放军理工大学气象海洋学院硕士研究生.主要研究方向为遥感图像处理和分析.本文通信作者.E-mail:lgdxmy@163.com

Building Segmentation of Spaceborne SAR Images Based on Energy Minimization

Funds: 

National Natural Science Foundation of China 41275029

National Natural Science Foundation of China 41175025

National Natural Science Foundation of China 61473310

National Natural Science Foundation of China 41174164

National Natural Science Foundation of China 41305138

China Research and Development Special Fund for Public Welfare Industry (Meteorology) GYHY201306068

More Information
    Author Bio:

    Professor at the Institute of Meteorology and Oceanography, PLA University of Science and Technology. His research interest covers computer vision, medical image processing, and remote sensing image analysis

    Professor at the Institute of Meteorology and Oceanography, PLA University of Science and Technology. His research interest covers hydromechanics and nolinear problem theory study in atmosphere science

    Master student at the Institute of Meteorology and Oceanography, PLA University of Science and Technology and engineer of Unit No. 96427 of PLA. His research interest covers remote sensing image processing and analysis

    Corresponding author: MENG Yong Master student at the Institute of Meteorology and Oceanography, PLA University of Science and Technology. His research interest covers remote sensing image processing and analysis. Corresponding author of this paper
  • 摘要: 针对星载合成孔径雷达 (Synthetic aperture radar, SAR) 图像信噪比低、建筑物目标几何变形大以及周围背景复杂的特点, 本文提出了一种基于能量最小化的星载SAR图像建筑物分割方法.基于星载SAR图像数据构造条件概率能量项, 推动变形曲线向建筑物目标边界演化; 在能量泛函模型中定义长度能量项以保证变形曲线的平滑; 在水平集方法获取的SAR图像初始分割结果的基础上, 以高分辨率光学遥感影像中建筑物目标的轮廓作为先验信息, 构造先验形状能量项约束曲线在第二阶段的演化, 最终实现SAR图像建筑物的分割.实验结果表明, 该方法显著提高了建筑物目标轮廓的分割精度.
  • 图  1  分割模型流程图

    Fig.  1  The flow chart of segmentation model

    图  2  待分割图像及其先验形状

    Fig.  2  Original image and its prior shape

    图  3  不同方法对仿真图像的分割结果

    Fig.  3  Segmentation results by different methods

    图  4  不同斑点噪声的分割结果

    Fig.  4  Segmentation results of different speckle noise images

    图  5  " H "状楼的SAR图像和全色波段图像及其先验形状

    Fig.  5  SAR image of the building with " H " contour, its corresponding panchromatic image and the prior shape

    图  6  不同方法对" H "字楼的分割结果

    Fig.  6  Segmentation results of the building with " H " contour by different methods

    图  7  " L "状楼的SAR图像和全色波段图像及其先验形状

    Fig.  7  SAR image of the building with " L " contour, its corresponding panchromatic image and the prior shape

    图  8  不同方法对" L "状楼的分割结果

    Fig.  8  Segmentation results of the building with " L " contour by different methods

    图  9  体育馆的SAR图像和全色波段图像及其先验形状

    Fig.  9  The SAR image, its corresponding panchromatic image and the prior shape of the gymnasium

    图  10  不同方法对体育馆的分割结果

    Fig.  10  Segmentation results of the gymnasium by different methods

    表  1  实验结果比较

    Table  1  Comparison of segmentation results

    实验名称 CV方法 Ben方法 Sui方法 本文方法
    精度 (%) 耗时 (s) 精度 (%) 耗时 (s) 精度 (%) 耗时 (s) 精度 (%) 耗时 (s)
    合成图像 97.4 15.4 97.4 21.8 97.3 16.9 99.4 113.7
    " H "状楼 28.7 133.9 57.3 82.9 84.0 52.9 89.6 116.1
    " L "状楼 13.4 162.3 21.7 20.8 62.9 13.7 91.5 67.1
    体育馆 82.0 326.2 76.1 88.0 86.3 101.9 94.1 206.1
    平均值 55.4 159.5 63.1 53.3 82.6 46.4 93.7 125.8
    下载: 导出CSV
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  • 收稿日期:  2015-07-20
  • 录用日期:  2015-11-10
  • 刊出日期:  2016-02-01

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