期刊
INFORMATION FUSION
卷 27, 期 -, 页码 150-160出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.inffus.2015.06.006
关键词
Image fusion; Spectral information; Spatial information; Principal component analysis; Optimal filter
High correlation among the neighboring pixels, both spectrally and spatially in a multispectral image makes it indispensable to use relevant data transformation approaches, before performing image fusion. The principal component analysis (PCA) method has been a popular choice for the spectral transformation. To propose a new consistent data transformation method in spatial domain, this paper applies the PCA transform to the spatial information of the neighboring pixels. Owing to the fact that the coefficients of PCA are obtained from statistical properties of data, they are adaptive and robust. Then, a new hybrid algorithm is proposed combining the spectral PCA and spatial PCA methods, by an optimal filter to make the synthesized result more similar to what the corresponding multisensors would observe at the high-resolution level. The evaluation of the pan-sharpened images, using global validation indexes, reveals that the proposed approach improves the fusion quality compared with six state of the art fusion methods. (C) 2015 Elsevier B.V. All rights reserved.
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