In the field of the remote sensing, the introduction of high resolution satellite sensors has required the development of several data fusion approaches. Two kinds of images are usually acquired: multispectral and panchromatic. The first group has a lower spatial resolution but accurate spectral information while the second presents a higher spatial resolution with a longer band acquisition range. Pan-sharpening permits to combine panchromatic and multispectral data to create new multispectral images with higher geometric resolution. In this paper nine different pan-sharpening methods are tested on WorldView-3 images: Brovey, Weighted Brovey, Gram Schmidt, IHS, Fast IHS, Multiplicative, Principal Component Analysis (PCA), Simple Mean and Zhang. With the aim to rank the techniques efficiency, visual inspections combined with quantitative evaluations are performed to test spectral qualities of the fused images. This is a difficult task because the quality of the fused image depends on the considered datasets: RMSE (Root Mean Square Error) and ERGAS (Relative Dimensionless Global Error) are the accuracy indices used for this scope.

Application of different pan-sharpening methods on WorldView-3 images

BELFIORE, OSCAR ROSARIO;MENEGHINI, CLAUDIO;PARENTE, Claudio;SANTAMARIA, Raffaele
2016

Abstract

In the field of the remote sensing, the introduction of high resolution satellite sensors has required the development of several data fusion approaches. Two kinds of images are usually acquired: multispectral and panchromatic. The first group has a lower spatial resolution but accurate spectral information while the second presents a higher spatial resolution with a longer band acquisition range. Pan-sharpening permits to combine panchromatic and multispectral data to create new multispectral images with higher geometric resolution. In this paper nine different pan-sharpening methods are tested on WorldView-3 images: Brovey, Weighted Brovey, Gram Schmidt, IHS, Fast IHS, Multiplicative, Principal Component Analysis (PCA), Simple Mean and Zhang. With the aim to rank the techniques efficiency, visual inspections combined with quantitative evaluations are performed to test spectral qualities of the fused images. This is a difficult task because the quality of the fused image depends on the considered datasets: RMSE (Root Mean Square Error) and ERGAS (Relative Dimensionless Global Error) are the accuracy indices used for this scope.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/52744
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