A New Scheme for Fusion of Multispectral and Panchromatic Images Based on Residual Error

Abstract

AbstractThe standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and low-resolution multispectral images because they can distort the spectral characteristics of the multispectral data.This paper proposes a new scheme based on residual error for fusion of such images. By merging high-resolution residual error extracted from panchromatic image and low-resolution residual errors from multispectral images through principal component analysis (PCA), the high-resolution residual errors of multispectral images can be restored. We point out that our scheme successfully solves the problem of spectral distortion. Finally, the performances of the proposed scheme are demonstrated experimentally, and the comparisons of the performances with standard IHS (intensity-hue-saturation), PCA, and wavelet transform-based fusion methods are made.KeywordsPrincipal Component AnalysisResidual ErrorImage FusionMultispectral ImageSpatial DetailThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.