THE OPTIMIZED BLOCK-REGRESSION-BASED FUSION ALGORITHM FOR PANSHARPENING OF VERY HIGH RESOLUTION SATELLITE IMAGERY

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLI-B7; pp. 739 - 746
• Main Authors: Zhang, J. X., Yang, J. H., Reinartz, P.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 21.06.2016; Copernicus Publications
• Subjects: Algorithms; High resolution; Image enhancement; Image resolution; Imagery; Mathematical models; Quantitative analysis; Regression analysis; Remote sensing; Resolution; SAR (radar); Satellite imagery; Satellites; Sharpening; Spaceborne remote sensing
• ISSN: 2194-9034; 1682-1750; 1682-1777; 2194-9034
• DOI: 10.5194/isprs-archives-XLI-B7-739-2016

Pan-sharpening of very high resolution remotely sensed imagery need enhancing spatial details while preserving spectral characteristics, and adjusting the sharpened results to realize the different emphases between the two abilities. In order to meet the requirements, this paper is aimed at providing an innovative solution. The block-regression-based algorithm (BR), which was previously presented for fusion of SAR and optical imagery, is firstly applied to sharpen the very high resolution satellite imagery, and the important parameter for adjustment of fusion result, i.e., block size, is optimized according to the two experiments for Worldview-2 and QuickBird datasets in which the optimal block size is selected through the quantitative comparison of the fusion results of different block sizes. Compared to five fusion algorithms (i.e., PC, CN, AWT, Ehlers, BDF) in fusion effects by means of quantitative analysis, BR is reliable for different data sources and can maximize enhancement of spatial details at the expense of a minimum spectral distortion.