Spatio-Spectral Multichannel Reconstruction from few Low-Resolution Multispectral Data

Hadj-Youcef, Mohamed Amine; Orieux, François; Fraysse, Aurélia; Abergel, A.

doi:10.23919/eusipco.2018.8553166

“…where only the data fitting term related to the MS image is considered. Similarly, the second version, called HS-only and similar to [45], solves the following HS super-resolution problem including only the data fitting term related to the HS imagê…”

Section: Compared Methodsmentioning

confidence: 99%

Hyperspectral and Multispectral Image Fusion Under Spectrally Varying Spatial Blurs – Application to High Dimensional Infrared Astronomical Imaging

Guilloteau

¹

,

Oberlin

²

,

Berné

³

et al. 2020

IEEE Trans. Comput. Imaging

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Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades. Current instrumental and observing time constraints allow direct acquisition of multispectral images, with high spatial but low spectral resolution, and hyperspectral images, with low spatial but high spectral resolution. To enhance scientific interpretation of the data, we propose a data fusion method which combines the benefits of each image to recover a high spatio-spectral resolution datacube. The proposed inverse problem accounts for the specificities of astronomical instruments, such as spectrally variant blurs. We provide a fast implementation by solving the problem in the frequency domain and in a low-dimensional subspace to efficiently handle the convolution operators as well as the high dimensionality of the data. We conduct experiments on a realistic synthetic dataset of simulated observation of the upcoming James Webb Space Telescope, and we show that our fusion algorithm outperforms state-of-the-art methods commonly used in remote sensing for Earth observation.

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“…The first one, called MS-only, considers a unique data fitting term associated with the MS image in (3). Thus, this problem is equivalent to the spectral reconstruction of the MS image, similarly to [28]. The second one, HS-only, can be seen as a hyperspectral super-resolution method as only the data fitting term related to the HS image is kept.…”

Section: B Compared Methodsmentioning

confidence: 99%

Fusion of hyperspectral and multispectral infrared astronomical images

Guilloteau

¹

,

Oberlin

²

,

Berné

³

et al. 2020

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

1

0

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This paper presents a data fusion method dedicated to high dimensional astronomical imaging. The fusion process reconstructs a high spatio-spectral resolution datacube, taking advantage of a multispectral observation image with high spatial resolution and a hyperspectral image with high spectral resolution. We define a regularized inverse problem accounting for the specificities of the astronomical observation instruments, in particular spectrally variant blurs. To handle convolution operators as well as the high dimensionality of the data, the problem is solved in the frequency domain and in a lowdimensional subspace. The fusion model is evaluated on simulated observations of the Orion Bar and shows excellent spatial and spectral reconstructions of the observed scene.

show abstract

“…where only the data fitting term related to the MS image is considered. Similarly, the second version, called HS-only and similar to [33], solves the following HS super-resolution problem including only the data fitting term related to the HS image…”

Section: Compared Methodsmentioning

confidence: 99%

Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging

Guilloteau¹,

Oberlin²,

Berné³

et al. 2019

Preprint

0

View full text Add to dashboard Cite

Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades. Current instrumental and observing time constraints allow direct acquisition of multispectral images, with high spatial but low spectral resolution, and hyperspectral images, with low spatial but high spectral resolution. To enhance scientific interpretation of the data, we propose a data fusion method which combines the benefits of each image to recover a high spatio-spectral resolution datacube. The proposed inverse problem accounts for the specificities of astronomical instruments, such as spectrally variant blurs. We provide a fast implementation by solving the problem in the frequency domain and in a low-dimensional subspace to efficiently handle the convolution operators as well as the high dimensionality of the data. We conduct experiments on a realistic synthetic dataset of simulated observation of the upcoming James Webb Space Telescope, and we show that our fusion algorithm outperforms state-of-the-art methods commonly used in remote sensing for Earth observation.

show abstract

Spatio-Spectral Multichannel Reconstruction from few Low-Resolution Multispectral Data

Cited by 6 publications

References 21 publications

Hyperspectral and Multispectral Image Fusion Under Spectrally Varying Spatial Blurs – Application to High Dimensional Infrared Astronomical Imaging

Hyperspectral and Multispectral Image Fusion Under Spectrally Varying Spatial Blurs – Application to High Dimensional Infrared Astronomical Imaging

Fusion of hyperspectral and multispectral infrared astronomical images

Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging

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