Image Fusion Using the Ehlers Spectral Characteristics Preservation Algorithm

Abstract

Image fusion is a technique that is used to combine the spatial structure of a high-resolution panchromatic image with the spectral information of a low-resolution multispectral image to produce a high-resolution multispectral image. Currently, image fusion techniques via color or statistical transforms such as the Intensity-HueSaturation (IHS) and principal component (PC) methods are still widely used. These methods create multispectral images of higher spatial resolution but usually at the cost of color distortions in the fused images. This is especially true if the wavelength range of the panchromatic image does not correspond to that of the employed multispectral bands or for multitemporal/multisensoral fusion. To overcome the color distortion problem, a number of new fusion methods have been developed over the last years. One of these is the Ehlers fusion algorithm, which is based on an IHS transform coupled with adaptive filtering in the Fourier domain. This method preserves the spectral characteristics of the lower spatial resolution multispectral images for single-sensor, multi-sensor, and multi-temporal fusion. A comparison between this method and three sophisticated new fusion techniques that are available in commercial image processing software is presented in this paper using multitemporal multi-sensor fusion with SPOT multispectral and Ikonos panchromatic datasets as well as single-sensor single-date multispectral and panchromatic Quickbird data. The fused images are compared visually and with statistical methods that are objective, reproducible, and quantitative.It can be shown that the sophisticated methods such as Gram Schmidt fusion, CN spectral sharpening, and the modified IHS provide good results in color preservation for single sensor fusion. For multi-temporal multi-sensor fusion, however, these methods produce significant changes in spectral characteristics for the fused datasets. This is not the case for the Ehlers fusion algorithm, which shows no recognizable color distortion even for multi-temporal and multi-sensor datasets.

Review and Performance Comparison of Pansharpening Algorithms for RASAT Images

“…After the low-pass filtering of the intensity component and the high pass filtering of the PAN image, inverse Fourier transform is applied to the filter outputs. Filtered PAN image is added to the filtered intensity component in the IHS domain and inverse IHS transformation is applied in order to obtain the pansharpened image [35].…”

A Pan-Sharpening Method Based on Guided Image Filtering: A Case Study Over Gf-2 Imagery

“…Four widely used metrics are selected for quantitative assessment, they are the entropy, the correlation coefficient (CC) (Klonus, 2007), the universal image quality index (UIQI) (Wang, 2002) and the relative dimensionless global error in synthesis (ERGAS) (Ranchin, 2000). The resampled MS image is taken as the reference image.…”

Hyperspectral Image Sharpening Based on Ehlers Fusion

“…The ideal way is to remove the high frequency part from the intensity component and replace it with the high frequency part from the pan image. At the end, the new intensity component together with the original hue and saturation are transformed back to Red Green and Blue (RGB) space (Klonus and Ehlers, 2007). This algorithm needs to be adjusted to fuse hyperspectral images.…”