Evaluation of Pan-Sharpening Methods

Melissa Strait, Sheida Rahmani, Daria Markurjev Faculty Advisor: Todd Wittman UCLA Department of Mathematics August 2008 Abstract Pan-sharpening combines a low-resolution color multispectral image with a high-resolution grayscale panchromatic image to create a high-resolution fused color image. In this paper we examine five different pan-sharpening methods: IHS, PCA, Wavelet fusion, P+XS, and VWP and evaluate their effectiveness. Additionally, we propose an extension to the IHS pan-sharpening method to improve the resulting spectral quality. In order to compare the method results we evaluate spatial and spectral qualities by relying on both visual inspection and metric performance data. Our results indicate that VWP is most effective in preserving spectral data, while IHS methods produce images with the best spatial quality.