The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
A comparative analysis of image fusion methods
Abstract
There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.
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Cited by
Thomas, C., Ranchin, T., Wald, L., & Chanussot, J. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.912448 · Cited by 632
A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement
Choi, J., Yu, K., & Kim, Y. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2051674 · Cited by 580
Image fusion based on pixel significance using cross bilateral filter
Shreyamsha Kumar, B. K. (2015) · Signal, Image and Video Processing · doi:10.1007/s11760-013-0556-9 · Cited by 567
Bayesian Data Fusion for Adaptable Image Pansharpening
Fasbender, D., Radoux, J., & Bogaert, P. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.917131 · Cited by 285
Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution
Akhtar, N., Shafait, F., & Mian, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_5 · Cited by 284
Metadata
- Authors
- Wang, Z., Ziou, D., Armenakis, C., Li, D., & Li, Q.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2005-06
- Volume
- 43
- Issue
- 6
- Pages
- 1391-1402
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2136401825
- Cited by
- 852
- References
- 24
- FWCI
- 83.64
- Type
- journal article
- Language
- en
- Open access
- closed
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