An adaptive PCA-based approach to pan-sharpening

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.

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