DocumentCode
1797377
Title
A new image fusion strategy based on target segmentaion
Author
Jian-Wei Liu
Author_Institution
Sch. of Sci., Xi´an Technol. Univ., Xi´an, China
Volume
1
fYear
2014
fDate
13-16 July 2014
Firstpage
159
Lastpage
162
Abstract
By analyzing the characteristics of target in multi spectral image and panchromatic image, a new fusion strategy based on target segmentation is proposed to fuse multi spectral image and panchromatic image. Firstly, Hue-Saturation-Intensity (HSI) transform is performed on the multi spectral image. Secondly, the intensity component by HSI transform is segmented by the expectation maximization (EM) algorithm and the panchromatic image is segmented by fuzzy C means (FCM) clustering algorithm, and obtains the better target area, followed by effective filling of the target area to get the new intensity component. Finally, the new fusion image is obtained by inverse HSI transform. Compared with the traditional HSI method, experiment results shows that the proposed strategy not only increases information entropy and average gradient of fusion image, but also decreases spectral bias index. Therefore, the proposed strategy is better than traditional HSI method. It not only enhances spatial resolution of fusion image and obtains detailed and feature information, but also preserves spectral information of the original multi-spectral image well.
Keywords
expectation-maximisation algorithm; image fusion; image segmentation; inverse transforms; EM algorithm; FCM clustering algorithm; expectation maximization algorithm; fuzzy c means clustering algorithm; hue-saturation-intensity transform; image fusion strategy; inverse HSI transform; multi spectral image; panchromatic image; target segmentaion; Abstracts; Entropy; Image resolution; Image segmentation; Expectation maximization; Fuzzy C means clustering; Hue-Saturation-Intensity; Image fusion; Target segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2014 International Conference on
Conference_Location
Lanzhou
ISSN
2160-133X
Print_ISBN
978-1-4799-4216-9
Type
conf
DOI
10.1109/ICMLC.2014.7009110
Filename
7009110
Link To Document