Remote sensing image fusion based on PCA transform and wavelet transform

Yilihamu·Yaermaimaiti; Xie Lirong; Kong Jun

The traditional PCA image fusion can produce multi-spectral image information variable loss in remote image fusion. Aim to it, a new algorithm of remote sensing image fusion based on PCA and wavelet transform was proposed in this paper. Firstly, principal component transformation for multi -spectral image was performed by eigenvalues and eigenvectors in each wave band. Secondly, the first non principal component of non-gray image and multi spectral image were matched in histogram information. Finally, inverse PCA transform was carried out for three principal components to obtain the desired fusion image. Experimental results show the proposed algorithm does not only maintain multi spectral information but also enhanced the processed image details, and the processed image has better subjective visual effect and objective quantitative indicators.

1. College of Electrical Engineering,Xinjiang University,Urumqi 830047,China;

2. School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China

Received Date: 2013-11-10

Rev Recd Date: 2013-12-25

Publish Date: 2014-07-25

Key words:

Abstract: The traditional PCA image fusion can produce multi-spectral image information variable loss in remote image fusion. Aim to it, a new algorithm of remote sensing image fusion based on PCA and wavelet transform was proposed in this paper. Firstly, principal component transformation for multi -spectral image was performed by eigenvalues and eigenvectors in each wave band. Secondly, the first non principal component of non-gray image and multi spectral image were matched in histogram information. Finally, inverse PCA transform was carried out for three principal components to obtain the desired fusion image. Experimental results show the proposed algorithm does not only maintain multi spectral information but also enhanced the processed image details, and the processed image has better subjective visual effect and objective quantitative indicators.

HTML

Reference (19)

[1]	Liu Bin, Peng Jiaxiong. Multi-spectral image fusion method based on two channels non-separable wavelets [J]. Sciences, 2008,51(12): 2022-2032.
[2]
[3]
[4]	Petrusca L, Cattin P, De Luca V, et al. Hybrid ultrasound/ magnetic resonance simultaneous acquisition and image fusion for motion monitoring in the upper abdomen [J]. Investigative Radiology, 2013, 48(5): 333-340.
[5]
[6]	Nasrin Amini, E Fatemizadeh, Hamid Behnam. MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules [J]. Journal of Medical Engineering Technology, 2014, 38(4): 211-219.
[7]
[8]	Wang R, Du L. Infrared and visible image fusion based on random projection and sparse representation [J]. International Journal of Remote Sensing, 2014, 35(5): 1640-1652.
[9]
[10]	Wu Yu, Yu Tao, Xie Donghai, et al. Automatic registration of high resolution and multi-spectral temote sensing images [J]. Infrared and Laser Engineering, 2012, 41(12): 3285-3290. (in Chinese) 吴俣, 余涛, 谢东海, 等. 高分辨率多光谱遥感图像的自动配准[J]. 红外与激光工程, 2012, 41(12): 3285-3290.
[11]	Ma Donghui, Xue Qun, Chai Qi, et al. Infrared and visible images fusion method based on image information [J]. Infrared and Laser Engineering, 2011, 40(6): 1168-1171. (in Chinese) 马东辉, 薛群, 柴奇, 等. 基于图像信息的红外与可见光图像融合方法研究[J]. 红外与激光工程, 2011, 40(6): 1168-1171.
[12]
[13]	Liu Chunxiang, Guo Yongfei, Li Ning, et al. Composition and compression of satellitemulti-channel remote sensingimages [J]. Optics and Precision Engineering, 2013, 21(2): 445-453. (in Chinese) 刘春香, 郭永飞, 李宁, 等. 星上多通道遥感图像的实时合成压缩[J]. 光学精密工程, 2013, 21(2): 445-453.
[14]
[15]
[16]	Liu Zunyang, Wang Zirong, Yu Dabin, et a1. Extracting dominant colors of imitative pattern painting with CIEDF_2000 and pyramid FCM [J]. Infrared and Laser Engineering, 2010, 39(2): 367-371. (in Chinese) 刘尊洋, 王自荣, 余大斌, 等. 塔形FCM 和CIEDF_2000 的仿造迷彩主色提取方法[J]. 红外与激光工程, 2010, 39(2): 367-371.
[17]	Zhao Peng, Ni Guoqiang. Image fusion based on multi-scale soft morphological filters [J]. Journal of Optoelectronics Laser, 2009, 20(9): 1243-1247. (in Chinese) 赵鹏, 倪国强. 基于多尺度柔性形态学滤波器的图像融合 [J]. 光电子激光, 2009, 20(9): 1243-1247.
[18]
[19]	Wu Y H, Yan D, Ma M X, et al. An improved compressive sensing image fusion algorithm based on NSCT transform[J]. Applied Mechanics and Materials, 2014, 433: 306-309.

Remote sensing image fusion based on PCA transform and wavelet transform

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views