Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1307-1319.doi: 10.13229/j.cnki.jdxbgxb20180371

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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

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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

CLC Number: 

  • TP391

Fig.1

Comparison of grayscale"

Fig.2

Algorithm framework"

Fig.3

Fusion coefficient function"

Fig.4

1st group of test images and fusion results"

Fig.5

Comparison on details of fused images in Fig.4"

Fig.6

2nd group of test images and fusion results"

Table 1

Objective evaluation results of 1st group of fusion images"

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

Table 2

Objective evaluation results of 2nd group of fusion images"

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

Fig.7

3rd group of test images and fusion results"

Fig.8

4th group of test images and fusion results"

Fig.9

5th group of test images and fusion results"

Table 3

Objective evaluation results of 3rd group of fusion image"

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

Table 4

Objective evaluation results of 4th group of fusion image"

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

Table 5

Objective evaluation results of 5th group of fusion image"

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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