Improving Component Substitution Pansharpening Through Multivariate Regression of MS +Pan Data

• Published in: IEEE transactions on geoscience and remote sensing Vol. 45; no. 10; pp. 3230 - 3239
• Main Authors: Aiazzi, B., Baronti, S., Selva, M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Band spectra; Bands; Component substitution (CS) pansharpening; Data visualization; Degradation; Gram-Schmidt (GS) spectral sharpening; IKONOS satellite data; Image fusion; Image resolution; Image sensors; intensity-hue-saturation (IHS) transform; Mathematical models; multispectral (MS) imagery; Multivariate regression; Preprocessing; Principal component analysis; QuickBird images; Radiometry; Regression; Satellites; Spatial resolution; Spectra; Studies
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2007.901007

In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectral-sharpening method, which is implemented in environment for visualizing images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines.