DocumentCode
2604450
Title
Remote Sensing Image Fusion on Gradient Field
Author
Wen, Jianting ; Li, Yan ; Gong, Haifeng
Author_Institution
Sch. of Comput., South China Normal Univ.
Volume
3
fYear
0
fDate
0-0 0
Firstpage
643
Lastpage
646
Abstract
This paper presents two methods to fuse a low spatial resolution multispectral image and a high spatial resolution panchromatic one to produce a new multispectral image with high spatial resolution. First, the Poisson fusion method is developed based on minimizing the gradient difference between the synthesized image and the panchromatic image with boundary conditions sampled from the multispectral image. The fusion result can therefore be achieved by solving the Poisson equation with Dirichlet boundary conditions. Secondly, an optimal fusion technique, which minimizes the gradient difference and the color difference with respect to the panchromatic and multispectral images respectively is given and the result is induced by an iterative optimization algorithm. Both of them can be applied to color composites and individual bands. Their advantages of the fidelity to spectral property and the spatial resolution improvement over the HSI, Brovey, PCA and wavelet transform are convincingly demonstrated in the experiments from visual evaluation and statistical analysis
Keywords
Poisson equation; boundary-value problems; geophysics computing; gradient methods; image colour analysis; image resolution; optimisation; remote sensing; stochastic processes; Dirichlet boundary conditions; Poisson fusion; gradient field; high spatial resolution panchromatic image; iterative optimization; low spatial resolution multispectral image; remote sensing image fusion; Boundary conditions; Fuses; Image fusion; Iterative algorithms; Multispectral imaging; Poisson equations; Principal component analysis; Remote sensing; Spatial resolution; Wavelet analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location
Hong Kong
ISSN
1051-4651
Print_ISBN
0-7695-2521-0
Type
conf
DOI
10.1109/ICPR.2006.995
Filename
1699608
Link To Document