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Optical Engineering • Open Access

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images
Author(s): Weihua Sun; Bin Chen; David Messinger

Paper Abstract

Commercial multispectral satellite datasets, such as WorldView-2 and Geoeye-1 images, are often delivered with a high-spatial resolution panchromatic image (PAN) as well as a corresponding lower resolution multispectral image (MSI). Certain fine features are only visible on the PAN but are difficult to discern on the MSI. To fully utilize the high-spatial resolution of the PAN and the rich spectral information from the MSI, a pan-sharpening process can be carried out. However, difficulties arise in maintaining radiometric accuracy, particularly for applications other than visual assessment. We propose a fast pan-sharpening process based on nearest-neighbor diffusion with the aim to enhance the salient spatial features while preserving spectral fidelity. Our approach assumes that each pixel spectrum in the pan-sharpened image is a weighted linear mixture of the spectra of its immediate neighboring superpixels; it treats each spectrum as its smallest element of operation, which is different from the most existing algorithms that process each band separately. Our approach is shown to be capable of preserving salient spatial and spectral features. We expect this algorithm to facilitate fine feature extraction from satellite images.

Paper Details

Date Published: 24 January 2014
PDF: 11 pages
Opt. Eng. 53(1) 013107 doi: 10.1117/1.OE.53.1.013107
Published in: Optical Engineering Volume 53, Issue 1
Show Author Affiliations
Weihua Sun, Rochester Institute of Technology (United States)
Bin Chen, Rochester Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)


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