Multispectral Image Sharpening with Multiresolution Analysis and the MTF

High resolution panchromatic imagery can be used to increase the spatial resolution of low resolution spectral imagery through spatial/spectral sharpening techniques. Recently, sharpening techniques have been presented that use a multiresolution analysis by manipulating the images at different resolution scales that use a Laplacian pyramid or wavelet transform. This paper presents a model for sharpening multispectral images (MRA/MTF) that uses multiresolution analysis (MRA) to extract the high frequency information from the panchromatic image and matches the spatial response between imagery using a modulation transfer function (MTF) correction. When carefully executed, the MRA/MTF model is shown to provide a sharpened image with minimum spectral distortion and visually pleasing results. Multispectral data was used to evaluate the algorithm for sharpening 30 meter Landsat data with 1 meter aerial photography with comparison to other sharpening algorithms available within ERDAS Imagine and with ERIM’s sharpening algorithm called Sparkle. The algorithms were tested for spectral distortion by comparing the covariance between bands before and after sharpening.