Name
Abstract | ||
|---|---|---|
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 low-dimensional subspace. The fusion model is evaluated on simulated observations of the Orion Bar and shows excellent spatial and spectral reconstructions of the observed scene. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SAM48682.2020.9104393 | 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM) |
Keywords | DocType | ISSN |
Data fusion,hyperspectral imaging,high dimensional imaging,astrophysics,super-resolution,spectrally varying blur. | Conference | 1551-2282 |
ISBN | Citations | PageRank |
978-1-7281-1947-2 | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claire Guilloteau | 1 | 0 | 0.34 |
| Thomas Oberlin | 2 | 128 | 14.57 |
| Olivier Berne | 3 | 0 | 0.34 |
| Nicolas Dobigeon | 4 | 2070 | 108.02 |