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Proceedings Paper

An adaptive PCA-based approach to pan-sharpening
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Paper Abstract

A pixel in multispectral images is highly correlated with the neighboring pixels both spatially and spectrally. Hence, data transformation is performed before performing pan-sharpening. Principal component analysis (PCA) has been a popular choice for spectral transformation of low resolution multispectral images. Current PCA-based pan-sharpening methods make an assumption that the first principal component (PC) of high variance is an ideal choice for replacing or injecting it with high spatial details from the high-resolution histogram-matched panchromatic (Pan) image. However, this paper, using the statistical measures on the datasets, shows that the low-resolution first PC component is not always an ideal choice for substitution. This paper presents a new method to improve the quality of the resultant images that are obtained using the PCA-based pan-sharpening methods. This approach is based on adaptively selecting the PC component required to be replaced or injected with high spatial details. The pan-sharpened image obtained by the proposed method is evaluated using well-known quality indexes. Results show that the proposed method increases the quality of the resultant fused images when compared to the standard approach.

Paper Details

Date Published: 24 October 2007
PDF: 9 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 674802 (24 October 2007); doi: 10.1117/12.736674
Show Author Affiliations
Vijay P. Shah, Mississippi State Univ. (United States)
Nicholas H. Younan, Mississippi State Univ. (United States)
Roger L. King, Mississippi State Univ. (United States)


Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)

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