Abstract
Abstract. Preservation of the spectral characteristics in multispectral images is important in the development of pansharpening methods because it affects the accuracy of subsequent applications, such as visual interpretation, land cover classification, and change detection. The combinations of the spectral properties (observation wavelength and width of spectral bands) of multispectral and panchromatic images affect both the spatial and spectral quality of pansharpened images. Therefore, the clarification of the relations between spectral bands and quality of pansharpened image is important for improving our understanding of pansharpening methods, and for developing better schemes for image fusion. This study investigated the influence of the spectral waveband of panchromatic images on the image quality of multispectral (MS) images using simulated images produced from hyperspectral data. Panchromatic images with different spectral band position and multispectral images with degraded spatial resolution were generated from airborne visible/infrared imaging spectrometer (AVIRIS) images and pansharpened using seven methods: additive wavelet intensity, additive wavelet principal component, generalized Laplacian pyramid with spectral distortion minimization, generalized intensity-huesaturation (GIHS) transform, GIHS adaptive, Gram–Schmidt spectral sharpening, and block-based synthetic variable ratio. The pansharpened near-infrared band was visually and statistically compared with the non-degraded image. Wide variation in quality was identified visually within and between methods depending on the spectral wavelengths of the panchromatic images. Quantitative evaluations using three frequently used indices, the correlation coefficient, erreur relative globale adimensionnelle de synthèse (ERGAS), and the Q index, showed the individual behaviors of the pansharpening methods in terms of the spectral similarity in panchromatic and near-infrared, though all methods had similar qualities in the case with the lowest similarity. These findings are discussed in terms of the fundamentals and structures of the methods.
Highlights
Image fusion is one way to use remote sensing to improve our understanding of the Earth’s surface through the synthesis of huge volumes of satellite and geographical data
As spectral deformation can result from the difference in the spectral wavelengths of panchromatic and multispectral images, it is necessary to investigate the influence of the spectral characteristics on the quality of pansharpened images to improve pansharpening methods
Additive Wavelet Principal Component (AWPC) results in remarkable blurring compared with the other methods, and this was more pronounced at higher overlap rates
Summary
Image fusion is one way to use remote sensing to improve our understanding of the Earth’s surface through the synthesis of huge volumes of satellite and geographical data. Most pansharpening methods alter the spectral properties of multispectral images while improving the spatial resolution (Ehlers et al, 2010, Alparone et al, 2007). As spectral deformation can result from the difference in the spectral wavelengths of panchromatic and multispectral images, it is necessary to investigate the influence of the spectral characteristics on the quality of pansharpened images to improve pansharpening methods. Hyperspectral remote sensing or imaging spectroscopy can provide a smooth spectral curve of a target by using a set of higher spectral resolution detectors (Jensen, 2007). This spectral information is quite useful for detail analyses of landsurface features such as vegetation or mineral resources. A wide variety of multispectral images with different spectral responses gives effective information for algorithm development, data assimilation, and sensor design
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