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Fast multispectral pansharpening based on a hyper-ellipsoidal color space
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Paper Abstract

In this paper, we present a modified version of a popular component-substitution (CS) pansharpening method, namely the hyperspherical color space (HCS) fusion technique. Unlike other improvements of HCS, the proposed method is insensitive to the format of the data, either calibrated spectral radiance values or uncalibrated digital numbers (DNs), thanks to the use of a multivariate linear regression between the squares of the interpolated MS bands and the squared lowpass filtered Pan, in order to find out the intensity component peculiar of CS methods. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the color space hyper-ellipsoidal instead of hyper-spherical and the intensity component more similar to the lowpass-filtered Pan, such that the extracted detail, namely Pan minus intensity, is more accurate. Furthermore, before the regression is calculated, the interpolated MS bands are diminished by their minima, in order to build a multiplicative injection model with approximately de-hazed components, thereby benefiting from the haze correction, as for all methods exploiting the multiplicative model. Experiments on true GeoEye-1 images show consistent advantages over the baseline HCS and its improvements achieved over time, and a performance comparable with some of the most advanced methods.

Paper Details

Date Published: 7 October 2019
PDF: 12 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115507 (7 October 2019); doi: 10.1117/12.2533481
Show Author Affiliations
Bruno Aiazzi, Istituto di Fisica Applicata "Nello Carrara" (Italy)
Luciano Alparone, Univ. degli Studi di Firenze (Italy)
Alberto Arienzo, Istituto di Fisica Applicata "Nello Carrara" (Italy)
Univ. degli Studi di Firenze (Italy)
Andrea Garzelli, Univ. degli Studi di Siena (Italy)
Simone Lolli, Istituto di Metodologie per l'Analisi Ambientale (Italy)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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