Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21744
Title: Remote sensing image fusion using multiscale mapped LS-SVM
Authors: Zheng, S
Shi, WZ 
Liu, J
Tian, J
Keywords: Image fusion
Mapped least-squares support vector machine (mapped LS-SVM)
Multiscale Gaussian radial basis functions (RBF)
Multispectral (MS) imagery
Remote sensing
Support value transform (SVT)
Issue Date: 2008
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on geoscience and remote sensing, 2008, v. 46, no. 5, p. 1313-1322 How to cite?
Journal: IEEE transactions on geoscience and remote sensing 
Abstract: The panchromatic (Pan) sharpening of multispectral (MS) bands is an important technique in the various applications of satellite remote sensing. This paper presents an MS Pansharpening method using the proposed multiscale mapped least-squares support vector machine (LS-SVM). Under the LS-SVM framework, the salient features underlying the image are represented by support values, and the support value transform (SVT) is developed for image information extraction. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The support value analysis is implemented by using a series of multiscale support value filters that are deduced from the mapped LS-SVM with multiscale Gaussian radial basis function kernels. Experiments are carried out on very high resolution QuickBird MS + Pan data. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed MS Pan-sharpening method performs comparable to the state-of-the-art in terms of the pertained quantitative quality evaluation indexes, such as the Spectral Angle Mapper, relative dimensionless global error in synthesis (ERGAS), modulation-transfer-function-based tool and quality index (Q4), etc. The SVT is an effective tool for remote sensing image fusion.
URI: http://hdl.handle.net/10397/21744
ISSN: 0196-2892
EISSN: 1558-0644
DOI: 10.1109/TGRS.2007.912737
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