基于协同经验小波变换的遥感图像融合

doi:10.13229/j.cnki.jdxbgxb20180371

摘要/Abstract

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

李雄飞,宋璐,张小利. 基于协同经验小波变换的遥感图像融合[J]. 吉林大学学报(工学版), 2019, 49(4): 1307-1319.

Xiong-fei LI,Lu SONG,Xiao-li ZHANG. Remote sensing image fusion based on cooperative empirical wavelet transform[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1307-1319.

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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

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

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

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

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