&lt;title&gt;Demonstration of the accuracy of improved-resolution hyperspectral imagery&lt;/title&gt;

Vrabel, James C.; Doraiswamy, P. C.; McMurtrey, J. E.; Stern, Alan J.

doi:10.1117/12.478790

Cited by 35 publications

(16 citation statements)

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“…The corresponding band segments are processed together in the following manner: Each input band is multiplied by the sharpening band and then normalized by dividing it by the sum of the input bands in the segment (Vrabel et al, 2002a(Vrabel et al, , 2002b:…”

Section: Cn Spectral Sharpeningmentioning

confidence: 99%

Image Fusion Using the Ehlers Spectral Characteristics Preservation Algorithm

Klonus

Ehlers

2007

GIScience & Remote Sensing

106

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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.

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Section: Cn Spectral Sharpeningmentioning

confidence: 99%

Image Fusion Using the Ehlers Spectral Characteristics Preservation Algorithm

Klonus

Ehlers

2007

GIScience & Remote Sensing

106

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“…Preliminary visual inspections of the images showed the advantage of the panchromatic band at capturing very fine spatial details of individual dry forest trees which were not resolved well on the 4 m spatial resolution images. Thus, the panchromatic image was fused to the multispectral image to derive a 1-m multispectral image using the spectral-sharpening technique of Vrabel (2000) and Vrabel et al (2002), who demonstrated that a pan-sharpened multispectral image maintained the radiometric accuracy of the original bands.…”

Section: Ikonos-2 Fine Resolution Imagerymentioning

confidence: 99%

An assessment of Hawaiian dry forest condition with fine resolution remote sensing

Morales

Miura

Idol

2008

Forest Ecology and Management

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“…Over the years, various image fusion processes have been developed for improving spatial and spectral resolutions of RS data sets based on intensity-hue-saturation (IHS) (Choi, 2006;Karathanassi et al, 2007), color normalized (CN) Brovey (Du et al, 2007;Bovolo et al, 2010), and principal component analysis (PCA) (Shah et al, 2008). Other methods such as Gram-Schmidt (GS) (Kumar et al, 2009), CN-spectral (Vrabel et al, 2002), and Ehler (Ehler et al, 2010), high pass fusion (HPF) (Wald et al, 1997) rely on the intensity modulation. In addition, several researchers have proposed the use of wavelet transform (Shi et al, 2005;Acerbi et al, 2006) or discrete wavelet transform to extract geometric edge information from PI images.…”

Section: Introductionmentioning

confidence: 99%