Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2012, Vol. 24 Issue (3) : 44-49     DOI: 10.6046/gtzyyg.2012.03.09
Technology and Methodology |
An Improved Wavelet Transformation Image Fusion Method and Evaluation of Its Fusion Result
DONG Zhang-yu1,2, ZHAO Ping3,4, LIU Dian-wei1, WANG Zong-ming1, TANG Xu-guang1,2, Liu Jing-yi5
1. Northeast Institute of Geography and Agricultural Ecology, Changchun 130012, China;
2. Graduate University of Chinese Academy of Science, Beijing 100049, China;
3. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241003, China;
4. College of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China;
5. College of Information Engineering, China University of Geosciences, Wuhan 430074, China
Download: PDF(1288 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  On the basis of a summary and analysis of wavelet transformation remote sensing image fusion method, in combination with the advantages of local variance and partial differential weighted criterion, and in the light of the deficiencies of wavelet transform method in enhancing space texture information, this paper has proposed an improved wavelet transformation remote sensing image fusion algorithm. With IKONOS multi-spectral and panchromatic as fusion experiments data, the new algorithm fusion effect was comprehensively evaluated from the subjective, the objective and the object-oriented classification accuracy. The results show that the improved algorithm combined with advantages of the wavelet transform and local algorithm is quite satisfactory. It greatly remedies the defects of traditional wavelet fusion method in remote sensing image texture information loss and serves as a kind of efficient remote sensing image fusion method. With the utilization of the new image fusion method, the remote sensing image variance is raised from the original 98.28 to 164.32, the information entropy increases from 5.30 to 7.85, the average gradient rises from 1.972 to 8.807, and the image classification accuracy increases by 10.24%.
Keywords WorldView-2 remote sensing image      remote sensing investigation for mining development      cross-border of cave mouth      Xuanwei of Yunnan province     
:  TP75  
Issue Date: 20 August 2012
E-mail this article
E-mail Alert
Articles by authors
NAN Jun-xiang
ZHAO Zhi-fang
HONG You-tang
DU Rui-ling
Cite this article:   
NAN Jun-xiang,ZHAO Zhi-fang,HONG You-tang, et al. An Improved Wavelet Transformation Image Fusion Method and Evaluation of Its Fusion Result[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 44-49.
URL:     OR
[1] 贾永红.多源遥感影像数据融合技术[M].北京:测绘出版社,2005:3-10. Jia Y H.Multi-source Remote Sensing Image Data Fusion Technology[M].Beijing:Science Press,2005:3-10(in Chinese).
[2] Zhang J X.Multi-source Remote Sensing Data Fusion: Status and Trends[J].International Journal of Image and Data Fusion,2010,1(1):5-24.
[3] Tupin F,Bloch I,Maitre H.A First Step Toward Automatic Interpretation of SAR Images Using Evidential Fusion of Several Structure Detectors[J].IEEE Trans Geosci Remote Sensing,1999,37(3):1327-1343.
[4] 孙家柄,刘继琳,李军.多源遥感影像融合[J].遥感学报,1998,2(1):47-49. Sun J B,Liu J L,Li J.Multi-source Remote Sensing Image Data Fusion[J].Journal of Remote Sensing,1998,2(1):47-49(in Chinese with English Abstract).
[5] 牟凤云,朱博勤,贺华中.基于小波变换的多源遥感数据融合方法研究[J].国土资源遥感,2003(4):30-34. Mou F Y,Zhu B Q,He H Z.Research on the Fusion of Multi-source Remotely Sensed Data Based on Wavelet Transform[J].Remote Sensing for Land and Resources,2003(4):30-34 (in Chinese with English Abstract).
[6] Chen Y,Xue Z Y,Blum R S.Theoretical Analysis of an Information-based Quality Measure for Image Fusion[J].Information Fusion,2008,9(2):161-175.
[7] Maslov I V,Gertner I.Multi-sensor Fusion: An Evolutionary Algorithm Approach[J].Information Fusion,2006,7(3):304–330.
[8] 贾永红,李德仁,孙家柄,等.四种IHS变换用于SAR与TM影像复合的比较[J].遥感学报,1998,2(2):103-106. Jia Y H,Li D R,Sun J B,et al.Comparison of HIS Transformation for Integrating SAR and TM Images[J].Journal of Remote Sensing,1998,2(2):103-106 (in Chinese with English Abstract).
[9] Li S T,Wang Y N,James T,et al.Using the Discrete Wavelet Frame Transform to Merge Landsat TM and SPOT Panchromatic Images[J].Information Fusion,2002,3(1):17-23.
[10] Li H,Manjunath B S,Mttra S K.Multi-sensor Image Fusion Using the Wavelet Transform[J].Graphical Models and Image Processing,1994,57(3):235-246.
[11] 李军,周月琴,李德仁.小波变换用于高分辨率全色影像与多光谱影像的融合研究[J].遥感学报,1999,3(2):116-121. Li J,Zhou Y Q,Li D R.Fusion of High-resolution Panchromatic and Multi-spectral Images by Using Wavelet Transform[J].Journal of Remote Sensing,1999,3(2):116-121(in Chinese with English Abstract).
[12] Zhang Y,Hong G.An IHS and Wavelet Integrated Approach to Improve Pan-sharpening Visual Quality of Natural Color IKONOS and QuickBird Images[J].Information Fusion,2005,6(3):225-234.
[13] 强赞霞,彭嘉雄,王洪群.基于小波变换局部方差的遥感图像融合[J].华中科技大学学报:自然科学版,2003,31(6):89-91. Qiang Z X,Peng J X,Wang H Q.Remote Sensing Image Fusion Based on Local Deviation of Wavelet Transform[J].J Huazhong Univ of Sci & Tech:Nature Science Version,2003,31(6):89-91 (in Chinese with English Abstract).
[14] 詹翔,周焰.一种基于局部方差的雾天图像增强方法[J].计算机应用,2007,27(2):510-512. Zhan X,Zhou Y.Algorithm Based on Local Variance to Enhance Contrast of Fog-degraded Image[J].Journal of Computer Applications,2007,27(2):510-512 (in Chinese with English Abstract).
[15] 胡晓东,沈占锋,王卫红,等.基于局部差异加权的遥感影像融合方法研究[J].武汉大学学报:信息科学版,2008,33(11):1162-1165. Hu X D,Shen Z F,Wang W H,et al.A New Method Based on Local Difference Powering for Remote Sensing Image Fusion[J].Geomatics and Information Science of Wuhan University,2008,33(11):1162-1165 (in Chinese with English Abstract).
[16] 徐佳,关泽群,何秀凤,等.基于传感器光谱特性的全色与多光谱图像融合[J].遥感学报,2009,13(1):97-102. Xu J,Guan Z Q,He X F,et al.Novemethod for Merging Panchromatic and Multi-spectral Images Based on Sensor Spectral Response[J].Journal of Remote Sensing, 2009,13(1):97-102(in Chinese with English Abstract).
[1] NAN Jun-xiang, ZHAO Zhi-fang, HONG You-tang, DU Rui-ling. Remote Sensing Investigation of Coal Mines in Xuanwei of Yunnan Province for Their Development[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 121-124.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech