Abstract. Earth observation at the local scale implies working on images with both high spatial and spectral resolutions. As the latter cannot be simultaneously provided by current sensors, hyperspectral pansharpening methods combine images jointly acquired by two different sensors, a panchromatic one providing high spatial resolution, and a hyperspectral one providing high spectral resolution, to generate an image with both high spatial and spectral resolutions. The main limitation in the fusion process is in presence of mixed pixels, which particularly affect urban scenes, and where large fusion errors may occur. Recently, the Spatially Organized Spectral Unmixing (SOSU) method was developed to overcome this limitation, delivering good results on agricultural and peri-urban landscapes, which contain a limited number of mixed pixels. This article presents a new version of SOSU, adapted to urban landscapes. It is validated on a Toulouse (France) urban dataset at a 1.6 m spatial resolution acquired by the HySpex instrument from the 2012 UMBRA campaign. A performance assessment is established, following Wald’s protocol and using complementary quality criteria. Visual and numerical (at the global and local scales) analyses of this performance are also proposed. Notably, in the VNIR domain, around 51 % of the mixed pixels are better processed by the presented version of SOSU than by the method used as a reference. This ratio is improved regarding shadowed areas in the reflective (52 %) and VNIR (57 %) domains.

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Hyperspectral pansharpening methods in the reflective domain are limited by the large difference between the visible panchromatic (PAN) and hyperspectral (HS) spectral ranges, which notably leads to poor representation of the SWIR (1.0–2.5 μm) spectral domain. A novel instrument concept is proposed in this study, by introducing a second PAN channel in the SWIR II (2.0–2.5 μm) spectral domain. Two extended fusion methods are proposed to process both PAN channels, namely, Gain-2P and CONDOR-2P: the first one is an extended version of the Brovey transform, whereas the second one adds mixed pixel preprocessing steps to Gain-2P. By following an exhaustive performance-assessment protocol including global, refined, and local numerical analyses supplemented by supervised classification, we evaluated the updated methods on peri-urban and urban datasets. The results confirm the significant contribution of the second PAN channel (up to 45% of improvement for both datasets with the mean normalised gap in the reflective domain and 60% in the SWIR domain only) and reveal a clear advantage for CONDOR-2P (as compared with Gain-2P) regarding the peri-urban dataset.

Hyperspectral pansharpening methods, which aim to combine hyperspectral and panchromatic images, yield limited performance for scenes whose strong spatial heterogeneity induces mixed pixels. The SOSU method has been designed to handle this limitation and provided good results on agricultural and peri-urban landscapes. However, its performance was reduced on more complex urban scenes, which contain a higher proportion of mixed pixels. This article presents a new version of this method, called SOSU-2021, adapted to better process urban scenes. SOSU-2021 is tested on an urban dataset at a 1.6 m spatial resolution. We obtain better numerical results than with the previous SOSU version, and in the worst case, 56 % of the mixed pixels are better or equally processed by SOSU-2021 than by the method used as a reference.