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基于边缘增强与光谱特性保持的Pan-sharpening融合模型

陈超迁 孟勇 杨平吕 罗其祥 周则明

陈超迁, 孟勇, 杨平吕, 罗其祥, 周则明. 基于边缘增强与光谱特性保持的Pan-sharpening融合模型. 自动化学报, 2019, 45(2): 374-387. doi: 10.16383/j.aas.2018.c170243
引用本文: 陈超迁, 孟勇, 杨平吕, 罗其祥, 周则明. 基于边缘增强与光谱特性保持的Pan-sharpening融合模型. 自动化学报, 2019, 45(2): 374-387. doi: 10.16383/j.aas.2018.c170243
CHEN Chao-Qian, MENG Yong, YANG Ping-Lv, LUO Qi-Xiang, ZHOU Ze-Ming. Pan-sharpening Model on Account of Edge Enhancement and Spectral Signature Preservation. ACTA AUTOMATICA SINICA, 2019, 45(2): 374-387. doi: 10.16383/j.aas.2018.c170243
Citation: CHEN Chao-Qian, MENG Yong, YANG Ping-Lv, LUO Qi-Xiang, ZHOU Ze-Ming. Pan-sharpening Model on Account of Edge Enhancement and Spectral Signature Preservation. ACTA AUTOMATICA SINICA, 2019, 45(2): 374-387. doi: 10.16383/j.aas.2018.c170243

基于边缘增强与光谱特性保持的Pan-sharpening融合模型

doi: 10.16383/j.aas.2018.c170243
基金项目: 

国家自然科学基金 61473310

国家自然科学基金 41174164

公益性行业(气象)科研专项 GYHY201306068

详细信息
    作者简介:

    陈超迁  国防科技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.E-mail:chenchaoqiannj@yahoo.com

    孟勇  国防科技大学气象海洋学院博士研究生.主要研究方向为遥感图像处理和分析.E-mail:lgdxmy@163.com

    杨平吕  国防科技大学气象海洋学院博士研究生.主要研究方向为遥感图像处理和分析.E-mail:yangpinglv@gmai.com

    罗其祥   国防科技大学气象海洋学院硕士研究生.主要研究方向为模式识别与图像处理.E-mail:qixiang_luo@aliyun.com

    通讯作者:

    周则明   国防科技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.本文通信作者.E-mail:zhou zeming@yahoo.com

Pan-sharpening Model on Account of Edge Enhancement and Spectral Signature Preservation

Funds: 

National Natural Science Foundation of China 61473310

National Natural Science Foundation of China 41174164

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

More Information
    Author Bio:

      Master student at the Institute of Meteorology and Oceanography, National University of Defense Technology. His research interest covers remote sensing image processing and analysis

     Ph. D. candidate at the Institute of Meteorology and Oceanography, National University of Defense Technology. His research interest covers remote sensing image processing and analysis

      Ph. D. candidate at the Institute of Meteorology and Oceanography, PLA University of Science and Technology. His research interest covers remote sensing image processing and analysis

      Master student at the Institute of Meteorology and Oceanography, National University of Defense Technology. His research interest covers pattern recognition and image processing

    Corresponding author: ZHOU Ze-Ming    Professor at the Institute of Meteorology and Oceanography, National University of Defense Technology. His research interest covers computer vision, medical image processing, and remote sensing image analysis. Corresponding author of this paper
  • 摘要: 为生成兼具高光谱质量与高空间质量的融合图像,本文提出了一种新的Pan-sharpening变分融合模型.通过拟合退化后的全色(Panchromatic,Pan)波段图像与低分辨率多光谱(Multispectral,MS)波段图像间的线性关系得到各波段MS图像的权重系数,计算从Pan图像抽取的空间细节;基于全色波段图像的梯度定义加权函数,增强了图像的强梯度边缘并对因噪声而引入的虚假边缘进行了抑制,有效地保持了全色波段图像中目标的几何结构;基于MS波段传感器的调制传输函数定义低通滤波器,自适应地限制注入空间细节的数量,显著降低了融合MS图像的光谱失真;针对Pan-sharpening模型的不适定性问题,引入L1正则化能量项,保证了数值解的稳定性.采用Split Bregman数值方法求解能量泛函的最优解,提高了算法的计算效率.QuickBird、IKONOS和GeoEye-1数据集上的实验结果表明,模型的综合融合性能优于MTF-CON、AWLP、SparseFI、TVR和MTF-Variational等算法.
    1)  本文责任编委 贾云得
  • 图  1  梯度幅值对比图

    Fig.  1  Comparison of gradient magnitude

    图  2  参数对融合结果的分析

    Fig.  2  The analysis of different parameters

    图  3  IKONOS融合结果

    Fig.  3  Original IKONOS images and pan-sharpening results using different methods

    图  4  IKONOS融合结果

    Fig.  4  Original IKONOS images and pan-sharpening results using different methods

    图  5  GeoEye-1融合结果

    Fig.  5  Original GeoEye-1 images and pan-sharpening results using different methods

    图  6  稀疏植被区域融合结果对比图

    Fig.  6  Pan-sharpening results of the MS images with sparse vegetated area

    图  7  中等植被区域融合结果对比图

    Fig.  7  Pan-sharpening results of the MS images with moderate vegetated area

    图  8  多植被区域融合结果对比图

    Fig.  8  Pan-sharpening results of the MS images with dense vegetated area

    表  1  IKONOS、QuickBird和GeoEye-1中的权重系数

    Table  1  The Weight coefficient of IKONOS, QuickBird, and GeoEye-1

    ${\alpha _1}$ ${\alpha _2}$ ${\alpha _3}$ ${\alpha _4}$
    IKONOS 0.4099 $-$0.0436 0.1154 0.4832
    QUICKBIRD 0.2361 0.1593 $-$0.0143 0.6238
    GeoEye-1 0.3787 0.1248 0.2304 0.2886
    下载: 导出CSV

    表  2  IKONOS、QuickBird和GeoEye-1在Nyquist频率处的MTF值

    Table  2  MTF gains at Nyquist cutoff frequency

    ${B}$ ${G}$ ${R}$ NIR PAN
    IKONOS 0.27 0.28 0.29 0.28 0.17
    QUICKBIRD 0.34 0.32 0.30 0.22 0.15
    GeoEye-1 0.33 0.36 0.40 0.34 0.16
    下载: 导出CSV

    表  3  QuickBird融合结果定量评价

    Table  3  Quality assessment of the fused images for QuickBird dataset

    ${sCC}$ ${ERGAS}$ ${SAM}$ ${QNR}$ ${D_\lambda}$ ${D_s}$
    MTF-CON 0.9074 1.1001 1.3404 0.7960 0.1286 0.0865
    SparseFI 0.9016 1.2000 1.3428 0.8800 0.0692 0.0545
    AWLP 0.9364 1.1306 1.3596 0.8002 0.1248 0.0857
    TVR 0.9360 1.4574 1.5448 0.8712 0.0538 0.0793
    MTF-Variational 0.9701 0.9920 1.1124 0.8428 0.0925 0.0713
    本文方法 0.9564 0.9695 1.0853 0.8816 0.0696 0.0525
    下载: 导出CSV

    表  4  IKONOS融合结果定量评价

    Table  4  Quality assessment of the fused images for IKONOS dataset

    ${sCC}$ ${ERGAS}$ ${SAM}$ ${QNR}$ ${D_\lambda}$ ${D_s}$
    MTF-CON 0.9196 3.4550 4.4411 0.7970 0.0998 0.1146
    SparseFI 0.9465 4.0197 4.3311 0.8274 0.0747 0.1058
    AWLP 0.9461 3.5247 4.3598 0.8125 0.0838 0.1131
    TVR 0.9886 4.3916 4.9391 0.7775 0.0819 0.1531
    MTF-Variational 0.9843 3.3412 3.8671 0.7994 0.0880 0.1234
    本文方法 0.9613 3.2632 3.6353 0.8432 0.0619 0.1012
    下载: 导出CSV

    表  5  GeoEye-1融合结果定量评价

    Table  5  Quality assessment of the fused images for GeoEye-1 dataset

    ${sCC}$ ${ERGAS}$ ${SAM}$ ${QNR}$ ${D_\lambda}$ ${D_s}$
    MTF-CON 0.9282 2.0588 2.3398 0.8009 0.1162 0.0938
    SparseFI 0.9185 2.4023 2.6090 0.8932 0.0572 0.0526
    AWLP 0.9465 1.9823 2.3003 0.8233 0.0986 0.0866
    TVR 0.9854 2.4718 2.5682 0.8546 0.0685 0.0826
    MTF-Variational 0.0.9827 1.8705 1.8881 0.8520 0.0788 0.0751
    本文方法 0.9515 1.8289 1.7632 0.9100 0.0406 0.0515
    下载: 导出CSV

    表  6  不同植被区域融合结果定量分析

    Table  6  Quality assessment of different areas of the fused images

    $sCC$ ERGAS $SAM$ $QNR$ ${D_\lambda}$ ${D_s}$
    稀疏植被区域 MTF-CON 0.9147 2.5807 2.1891 0.6347 0.1598 0.2446
    AWLP 0.9381 2.5409 2.1481 0.6914 0.1304 0.2049
    SparseFI 0.8889 2.3891 1.8947 0.6945 0.1206 0.2103
    TVR 0.9458 3.0313 2.5235 0.6096 0.2109 0.2275
    MTF-Variational 0.9777 2.3864 1.9506 0.6842 0.1458 0.1990
    本文方法 0.9725 2.3521 1.8318 0.7002 0.1122 0.2112
    中等植被区域 MTF-CON 0.9008 2.6798 2.5738 0.7178 0.1187 0.1855
    AWLP 0.9167 2.5791 2.5158 0.6894 0.1627 0.1767
    SparseFI 0.9135 2.4626 2.2991 0.6942 0.1520 0.1814
    TVR 0.9611 3.0606 2.8873 0.7503 0.1018 0.1647
    MTF-Variational 0.9687 2.4408 2.2390 0.7424 0.1183 0.1580
    本文方法 0.9755 2.4400 2.1686 0.7614 0.0883 0.1648
    多植被区域 MTF-CON 0.8900 4.1248 4.6158 0.5766 0.2528 0.2283
    AWLP 0.9602 4.0810 4.5489 0.5890 0.2396 0.2254
    SparseFI 0.9575 4.0404 4.5037 0.7407 0.1304 0.1483
    TVR 0.9857 4.7771 5.0041 0.6618 0.1589 0.2132
    MTF-Variational 0.9664 4.1813 4.6367 0.7865 0.1043 0.1219
    本文方法 0.9751 3.9230 3.7936 0.7879 0.1141 0.1106
    下载: 导出CSV
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  • 收稿日期:  2017-05-06
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