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This article presents an original method for the spatial resolution enhancement of satellite hyperspectral (HS) data by means of the Sentinel-2 visible and near infrared (VNIR) and short-wave infrared bands at 10 and 20 m spatial resolution. Presently, HS data are available from PRISMA (Italian acronym for HS precursor of the application mission) and Environmental Mapping and Analysis Program (EnMAP): both map the spectral interval of the solar radiation onto 240 and 224 bands, respectively, with 10 and 6.5/10 nm widths. A 5 m × 5 m panchromatic (PAN) band is also acquired by PRISMA. When the PAN band is unavailable, or better, the higher spatial resolution sharpening band is not unique, advantage can be taken from the hypersharpening protocol. First, the 20-m bands of Sentinel-2 are hypersharpened to 10 m by means of the four 10-m VNIR bands of the same instrument. Then, the 10-m hypersharpened bands of Sentinel-2 are used to sharpen the 30-m bands of PRISMA at 10 m as well, still according to the hypersharpening protocol. Eventually, the 10- m hypersharpened bands are pansharpened at 5 m by means of the PAN image, if available. Results show that for PRISMA the nested hypersharpening followed by pansharpening is better than plain HS pansharpening, both visually and according to full-scale indexes of spectral and spatial consistence. For EnMAP data, in which the PAN image is missing, the improvement of the fused data with respect to the original EnMAP and Sentinel-2 data has been quantified by means of two novel statistical indexes capable of measuring the spatial and intersensor consistencies between sharpened and sharpening data.

The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been proposed to tackle this problem, particularly those based on spectral unmixing and using parametric models. Nevertheless, the reported methods need a large number of parameters to address spectral variability, which inevitably yields a higher computation time compared to the standard hypersharpening methods. In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the Automated Extraction of Endmember Bundles (AEEB), and the sparsity-based method called Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL), is introduced. This new method called Hyperspectral Super-resolution with Spectra Bundles dealing with Spectral Variability (HSB-SV) was tested on both synthetic and real data. Experimental results showed that HSB-SV provides sharpened products with higher spectral and spatial reconstruction fidelities with a very low computational complexity compared to other methods dealing with spectral variability, which are the main contributions of the designed method. [ABSTRACT FROM AUTHOR]

The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been proposed to tackle this problem, particularly those based on spectral unmixing and using parametric models. Nevertheless, the reported methods need a large number of parameters to address spectral variability, which inevitably yields a higher computation time compared to the standard hypersharpening methods. In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the Automated Extraction of Endmember Bundles (AEEB), and the sparsity-based method called Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL), is introduced. This new method called Hyperspectral Super-resolution with Spectra Bundles dealing with Spectral Variability (HSB-SV) was tested on both synthetic and real data. Experimental results showed that HSB-SV provides sharpened products with higher spectral and spatial reconstruction fidelities with a very low computational complexity compared to other methods dealing with spectral variability, which are the main contributions of the designed method.

In this letter, hypersharpening is analyzed in depth by investigating some weaknesses present in its formulation. It is shown that the key formula of the synthesized band variant can be simplified under certain circumstances. In addition, a novel fusion schema is proposed. As a result, the gain factor adopted to weight the injected detail is computed in a different way. This schema can be applied to fuse a wide range of hyperspectral and multispectral data. In this letter, its effectiveness is demonstrated by taking into account the characteristics of WorldView-3 data. [ABSTRACT FROM AUTHOR]

This letter discusses whether the expanded multispectral image, i.e., the original multispectral image upsampled to the panchromatic scale, can be used during the assessment of the quality of pansharpened multispectral images. By considering Wald’s protocol, the authors demonstrate that the adoption of the expanded image as the reference is erroneous and brings a quality assessment of the fused images that is misleading. In addition, some recommendations about the valid role of the expanded image are provided. The discussion is supported by a quantitative analysis and visual comparisons. [ABSTRACT FROM PUBLISHER]

WorldView 3 (WV-3) is the first commercially deployed super-spectral, very high-resolution (HR) satellite. However, the resolution of the short-wave infrared (SWIR) bands is much lower than that of the other bands. In this letter, we describe four different approaches, which are combinations of pansharpening and hypersharpening methods, to generate HR SWIR images. Since there are no ground truth HR SWIR images, we also propose a new picture quality predictor to assess hypersharpening performance, without the need for reference images. We describe extensive experiments using actual WV-3 images that demonstrate that some approaches can yield better performance than others, as measured by the proposed blind image quality assessment model of hypersharpened SWIR images. [ABSTRACT FROM PUBLISHER]

Hypersharpening aims at combining an observable low-spatial resolution hyperspectral image with a high-spatial resolution remote sensing image, in particular a multispectral one, to generate an unobservable image with the high spectral resolution of the former and the high spatial resolution of the latter. In this paper, two such new fusion methods are proposed. These methods, related to linear spectral unmixing techniques, and based on nonnegative matrix factorization (NMF), optimize a new joint criterion and extend the recently proposed joint NMF (JNMF) method. The first approach, called gradient-based joint-criterion NMF (Grd-JCNMF), is a gradient-based method. The second one, called multiplicative JCNMF (Mult-JCNMF), uses new designed multiplicative update rules. These two JCNMF approaches are applied to synthetic and semireal data, and their effectiveness, in spatial and spectral domains, is evaluated with commonly used performance criteria. Experimental results show that the proposed JCNMF methods yield sharpened hyperspectral data with good spectral and spatial fidelities. The obtained results are compared with the performance of two NMF-based methods and one approach based on a sparse representation. These results show that the proposed methods significantly outperform the well-known coupled NMF sharpening method for most performance figures. Also, the proposed Mult-JCNMF method provides the results that are similar to those obtained by JNMF, with a lower computational cost. Compared with the tested sparse-representation-based approach, the proposed methods give better results. Moreover, the proposed Grd-JCNMF method considerably surpasses all other tested methods. [ABSTRACT FROM AUTHOR]

This paper aims at defining a new paradigm (hypersharpening) in remote sensing image fusion. In fact, due to the development of new instruments, thinking only in terms of pansharpening is reductive. Even though some expressions as hyperspectral (HS) pansharpening already exist, there is not a suitable definition when multispectral/hyperspectral data are used as source to extract spatial details. After defining the hypersharpening framework, we draw the readers’ attention to its peculiar characteristics, by proposing and evaluating two hypersharpening methods. Experiments are carried out on the data produced by the updated version of SIM-GA imager, designed by Selex ES, which is composed by a panchromatic camera and two spectrometers in the VNIR and SWIR spectral ranges, respectively. Owing to the different resolution factors among panchromatic, VNIR and SWIR data sets, we can apply hypersharpening to fuse SWIR data to VNIR resolution. Comparisons of hypersharpening with “traditional” pansharpening show hypersharpening is more effective. [ABSTRACT FROM PUBLISHER]