Fusion of Hyperspectral and Panchromatic Data by Spectral Unmixing in the Reflective Domain

Abstract

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.

Hyperspectral Pansharpening in the Reflective Domain with a Second Panchromatic Channel in the SWIR II Spectral Domain

“…In this study, two methods, Gain and CONDOR, presented and compared in previous work [44,45], were extended to process two PAN channels. These new methods are called Gain-2P and CONDOR-2P, respectively.…”

“…The resulting method is called SOSU [25]. We improved it in several steps [44,45], to efficiently process urban scenes. This led to the Combinatorial Optimisation for 2D ORganisation (CONDOR) method, as proposed in [45].…”

“…Indeed, as the PAN spectral domain is generally included in the (0.4-0.8 µm) one, spectral bands from the (0.8-2.5 µm) spectral domain are insufficiently represented. In our previous work [44,45], this limitation caused poor performance in the (1.0-2.5 µm) Short-Wave InfraRed (SWIR) spectral domain with the two selected methods, Gain and CONDOR. Furthermore, as the spectral radiance dynamic range is much higher in the (0.4-1.0 µm) VNIR domain than in the SWIR domain, these fusion methods give much more importance to the former at the expense of the latter.…”

“…Quality criteria are almost always applied to whole images and to the whole HS spectral domain [8]. However, it is crucial to distinguish between the spectral bands of the fused images inside and outside the PAN spectral domain, because the conclusions can largely differ [44], as explained above. Moreover, application of quality criteria is never focussed on specific areas of interest.…”

“…Furthermore, local (i.e., pixel-wise) evaluations, like error maps, are rarely proposed in the literature, whereas they are crucial to spatially identify error sources and provide relevant complementary observations [44]. Finally, performance assessments are hardly ever extended to the analysis of the fusion impact on application results (e.g., change detection and classification).…”