Satellite image fusion using RPCA combined IHS transform

TIAN Yun-xiang; WU Jiang-long; TIAN Xiao-lin

doi:10.3788/OPE.20152313.0504

您当前的位置：

首页 >

文章列表页 >

Satellite image fusion using RPCA combined IHS transform

更新时间：2020-08-13

- Satellite image fusion using RPCA combined IHS transform
- Optics and Precision Engineering Vol. 23, Issue 10z, Pages: 504-508(2015)
- 作者机构：
  
  1. 澳门科技大学, 资讯科技学院,澳门,999078
  2. 澳门科技大学月球与行星科学实验室/太空科学研究所,澳门,999078
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152313.0504
  CLC： TP751
- Received：21 March 2015，
  
  Revised：20 April 2015，
  
  Published：14 November 2015
- 稿件说明：
移动端阅览
田云翔, 武江龙, 田小林. 鲁棒主成份分析与IHS变换相结合的卫星图像融合[J]. 光学精密工程, 2015,23(10z): 504-508

TIAN Yun-xiang, WU Jiang-long, TIAN Xiao-lin. Satellite image fusion using RPCA combined IHS transform[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 504-508
田云翔, 武江龙, 田小林. 鲁棒主成份分析与IHS变换相结合的卫星图像融合[J]. 光学精密工程, 2015,23(10z): 504-508 DOI： 10.3788/OPE.20152313.0504.

TIAN Yun-xiang, WU Jiang-long, TIAN Xiao-lin. Satellite image fusion using RPCA combined IHS transform[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 504-508 DOI： 10.3788/OPE.20152313.0504.

摘要

由于传统的主成份分析(PCA)和IHS卫星图像融合方法会产生光谱信息损失和特征细节失真等现象

本文提出一种基于鲁棒PCA(RPCA)和IHS变换相结合的卫星图像融合方法。首先

对多光谱图像进行IHS变换

使其从RGB空间变换到IHS空间;对其中的I分量进行RPCA变换得到低秩矩阵L0和稀疏矩阵S0

并利用直方图匹配把全色图像匹配到L0分量上。然后

用匹配后的全色图像替换L0分量进行RPCA反变换。最后

进行IHS反变换得到融合图像。实验结果显示

本文算法获得的融合图像的信息熵分别为7.5275和7.4772

显示RPCA与IHS相结合的方法可以保留更多的光谱信息和空间特征细节

优于传统的IHS和PCA融合的方法。

Abstract

The traditional Principal Component Analysis(PCA) and IHS satellite image fusion technology may have spectral losing and feature detail distortion. A fusion method using Robust PCA(RPCA) combined with IHS transform was proposed to solve the problem. First

multi-spectral images were converted from a RGB model into an IHS model. The 'I' component of IHS was converted into low-rank matrix L0 and sparse matrix S0 by RPCA transform. Then

the histogram of panchromatic images was matched with the histogram of the L0 component

and the L0 component was replaced by the panchromatic image. Finally

the fused multi-spectral image was converted back to the RGB space. Testing results show that of the information entropies preserved by RPCA combined IHS method are 7.5275 and 7.4772

which means that the method proposed in the paper gives better results than conventional methods

such as PCA and IHS transform.

关键词

Keywords

references

伊力哈木·亚尔买买提,谢丽蓉, 孔军. 基于PCA 变换与小波变换的遥感图像融合方法[J]. 红外与激光工程,2014(7):2335-2340. YILIHAMU Y E M M T, XIE L R, KONG J. Remote sensing image fusion based on PCA transform and wavelet transform[J]. Infrared and Laser Engineering,2014(7):2335-2340.(in Chinese)

李新,秦世引. 一种具有高保真度光谱的遥感图像快速融合法[J]. 宇航学报,2010,31(8):2023-2028. LI X, QIN SH Y. An approach to fast fusion of remote sensing images with high fidelity of spectrum information[J].Journal of Astronautics,2010,31(8):2023-2028.(in Chinese)

张蕾,金龙旭,韩双丽,等. 采用非采样Contourlet变换与区域分类的红外和可见光图像融合[J]. 光学精密工程, 2015,23(3):810-818. ZHANG L, JIN L X, HAN SH L, et al.. Fusion of infrared and visual images based on non-sampled contourlet transform and region classification[J]. Opt. Precision Eng.,2015,23(3):810-818.(in Chinese)

戴光智,陈铁群,薛家祥,等. 小波图像融合改善超声图像分辨率[J]. 光学精密工程, 2008,16(11):2290-2295. DAI G ZH, CHEN T Q, XUE J X, et al.. Improvement of resolution for ultrasonic image based on wavelet image fusion[J]. Opt. Precision Eng.,2008,16(11):2290-2295.(in Chinese)

毛士艺,赵巍. 多传感器图像融合技术综述[J]. 北京航空航天大学学报,2002,28(5):512-518. MAO SH Y, ZHAO W. Comments on multisensor image fusion techniques[J]. Journal of Beijing University of Aeronautics and Astronautics,2002,28(5):512-518.(in Chinese)

周前祥,敬忠良,姜世忠.多源遥感影像信息融合研究现状与展望[J]. 宇航学报,2002,23(5):89-94. ZHOU Q X, JING ZH L, JING SH ZH. Comments on research and development of multi-sourse information fusion for remote sensing images[J].Journal of Astronautics,2002,23(5):89-94.(in Chinese)

杜洪,夏欣,据生根,等. 基于PCA图像压缩算法研究与实现[J]. 四川大学学报(自然科学版),2014,51(5):910-914. DU H, XIA X, JU SH G, et al.. Image compression based on PCA[J].Journal of Sichuan University(Natural Science Edition),2014,51(5):910-914.(in Chinese)

CANDES E J, LI X, MA Y, et al.. Robust principal component analysis[J].Journal of the ACM,2011,58:1-37.

DE LA TORRE F, BLACK M.J. Robust principal component analysis for computer vision[C]. International Conference on IEEE,2001,1:362-369.

WRIGHT J,MA Y, MAIRAL J, et al..Sparse representation for computer vision and pattern recognition[J].Proceedings of the IEEE,2010,98(6):1031-1044.

张永新. 多聚焦图像像素级融合算法研究[D].西安:西北大学,2014:1-39. ZHANG Y X. Research on pixel level fusion algorithms for multifocus image[D].Xi'an:Northwest University, 2014:1-39.(in Chinese)

余先川,吕中华,胡丹. 遥感图像配准技术综述[J]. 光学精密工程, 2013,21(11):2960-2972. YU X CH, LÜ ZH H, HU D. Review of remote sensing image registration techniques[J].Opt. Precision Eng.,2013,21(11):2960-2972.(in Chinese)

Views

438

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Polarized image feature fusion in power inspection

Infrared and visible image fusion based on multi-scale spatial attention complementary

Adaptive color compensation and multi-scale fusion for underwater image enhancement

X-ray image fusion for ancient bronze mirror based on intuitionistic fuzzy set entropy measure and salient feature detection

Conditional diffusion and multi-channel high-low frequency parallel fusion of infrared and visible light images

Related Author

NI Mengyao

PENG Yuanlong

HU Shang

YAN Longchuan

ZHENG Jinkun

CAO Danhua

ZHANG Yongxing

LIAN Bowen

Related Institution

School of Optical and Electronic Information， Huazhong University of Science and Technology

State Grid Information & Telecommunication Branch

State Grid JiangXi Electric Power Supply Co

National Key Laboratory of Adaptive Optics

Institute on Optics and Electronics Technology， Chinese Academy of Sciences

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰