吉林大学学报(工学版) ›› 2019, Vol. 49 ›› Issue (4): 1307-1319.doi: 10.13229/j.cnki.jdxbgxb20180371

• • 上一篇    

基于协同经验小波变换的遥感图像融合

李雄飞1,2(),宋璐2,张小利1()   

  1. 1. 吉林大学 计算机科学与技术学院,长春 130012
    2. 吉林大学 软件学院,长春 130012
  • 收稿日期:2018-04-19 出版日期:2019-07-01 发布日期:2019-07-16
  • 通讯作者: 张小利 E-mail:lxf@jlu.edu.cn;zhangxiaoli@jlu.edu.cn
  • 作者简介:李雄飞(1963-),男,教授,博士生导师. 研究方向:机器学习,信息融合,图像处理. E-mail:lxf@jlu.edu.cn
  • 基金资助:
    国家科技支撑计划项目(2012BAH48F02);国家自然科学基金项目(61801190);吉林省自然科学基金项目(20180101055JC);吉林省优秀青年人才基金项目(20180520029JH);中国博士后基金面上项目(2017M611323)

Remote sensing image fusion based on cooperative empirical wavelet transform

Xiong-fei LI1,2(),Lu SONG2,Xiao-li ZHANG1()   

  1. 1. College of Computer Science and Technology,Jilin University, Changchun 130012, China
    2. College of Software,Jilin University,Changchun 130012, China
  • Received:2018-04-19 Online:2019-07-01 Published:2019-07-16
  • Contact: Xiao-li ZHANG E-mail:lxf@jlu.edu.cn;zhangxiaoli@jlu.edu.cn

摘要:

针对多源遥感图像的融合问题,提出了一种基于协同经验小波变换的遥感图像融合方法。该算法首先对多源图像进行主成分分析获得共像;然后,对共像的强度分量做经验小波变换获得滤波器组;再利用这组滤波器对多光谱图像的强度分量和全色图像进行多尺度表示;最后经逆变换得到融合图像。该算法因采用协同自适应分解方法,有利于源图像高频与低频信息的分离,有效提高了遥感融合图像的清晰度。通过使用QuickBird卫星数据验证了算法的有效性,视觉感知和客观评价标准均表明该算法比其他同类算法有更好的优越性。

关键词: 计算机应用, 遥感图像融合, 经验小波变换, 协同性

Abstract:

A remote sensing image fusion method based on cooperative empirical wavelet transform to fuse multi-source remote sensing image is proposed. Firstly, the algorithm performed principal component analysis on multi-source images to obtain a common image. Secondly, empirical wavelet transform was performed on intensity components of the common image to obtain filter banks. Then these filters were used to represent multi-spectral image intensity components and panchromatic images in multiscale. Finally, fusion image was obtained by inverse transformation. The algorithm adopts the cooperative adaptive decomposition method, which is beneficial to separate high frequency and low frequency information of the source image, and effectively improve the clarity of the remote sensing fusion image. QuickBird satellite data verifies the effectiveness of the algorithm. Visual perception and objective evaluation criteria indicate that has better advantages than other similar algorithms.

Key words: computer application, remote sensing image fusion, empirical wavelet transform, cooperativity

中图分类号: 

  • TP391

图1

灰度图对比"

图2

算法框架图"

图3

融合系数函数"

图4

第1组测试图像及融合结果"

图5

图4中融合图像细节比较"

图6

第2组测试图像及融合结果"

表1

第1组融合图像客观评价结果"

算法 RMSE RASE SAM ERGAS PSNR SSIM Q ave
IHS 22.2254 52.4682 7.7981 12.5158 22.2606 0.8439 0.8978
DWT 19.3628 34.0643 6.1571 8.2142 23.4985 0.9244 0.9198
OHPF 20.2651 30.0090 2.8715 6.2870 22.3866 0.9534 0.9314
WT?SR 19.8009 37.7679 5.0812 8.6022 22.5431 0.9105 0.9249
NSCT 24.6146 38.4467 2.2639 12.1893 20.4806 0.8883 0.8658
EWT 18.1246 23.7163 1.8828 5.8821 23.7040 0.9641 0.9319

表2

第2组融合图像客观评价结果"

算法 RMSE RASE SAM ERGAS PSNR SSIM Q ave
IHS 30.3716 31.0853 1.9018 6.9990 20.2120 0.7297 0.8309
DWT 27.8557 18.1092 3.1537 4.7583 19.4589 0.9036 0.8993
OHPF 33.2604 22.4226 1.5624 4.6868 17.8567 0.9488 0.8917
WT?SR 28.1213 24.8624 3.7982 5.7716 19.6352 0.8319 0.8760
NSCT 24.6633 20.3793 3.1211 4.7806 20.5977 0.8832 0.9065
EWT 24.6866 12.5920 0.9886 3.1115 21.1044 0.9528 0.8991

图7

第3组测试图像及融合结果"

图8

第4组测试图像及融合结果"

图9

第5组测试图像及融合结果"

表3

第3组融合图像客观评价结果"

算法 RMSE RASE SAM ERGAS PSNR SSIM Q ave
IHS 31.5504 29.9152 2.1829 6.6315 18.5856 0.7969 0.8538
DWT 26.8600 15.8039 1.5048 3.9499 20.1744 0.9214 0.8906
OHPF 25.8266 17.7111 1.1558 3.9022 20.1410 0.9547 0.9152
WT?SR 26.5366 22.5844 3.4426 5.1998 19.9179 0.8693 0.8960
NSCT 33.9741 20.7034 1.1517 5.9854 17.9541 0.9020 0.8913
EWT 23.7549 13.4538 1.1795 3.2849 21.3614 0.9463 0.9154

表4

第4组融合图像客观评价结果"

算法 RMSE RASE SAM ERGAS PSNR SSIM Q ave
IHS 30.7291 29.0417 1.8867 7.2592 18.6423 0.7521 0.8056
DWT 22.3085 20.2051 4.0538 4.6172 21.3678 0.9031 0.9117
OHPF 29.5906 25.4255 2.2483 5.1827 18.7700 0.9425 0.9171
WT?SR 28.2686 27.5227 4.4231 6.2810 19.6222 0.8204 0.8532
NSCT 29.4142 23.3226 1.2483 6.6047 18.9528 0.8693 0.8788
EWT 22.2360 16.6535 1.3655 4.0650 21.5812 0.9246 0.9085

表5

第5组融合图像客观评价结果"

算法 RMSE RASE SAM ERGAS PSNR SSIM Q ave
IHS 25.7279 37.9812 2.3086 10.2538 20.5340 0.8483 0.8751
DWT 23.9356 33.4104 3.0016 8.1045 20.8430 0.8906 0.9043
OHPF 23.4535 25.9037 1.3578 5.5809 21.1087 0.9524 0.9251
WT?SR 21.0221 26.1958 2.3558 6.6291 22.0092 0.9328 0.9243
NSCT 19.9575 26.6552 3.0279 6.9528 22.3768 0.9266 0.9296
EWT 20.0664 23.9614 1.1480 6.0644 22.4562 0.9443 0.9302
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