3 research outputs found
Super-resolution of hyperspectral images using local spectral unmixing
International audienceFor many remote sensing applications it is preferable to have images with both high spectral and spatial resolutions. On this regards, hyperspectral and multispectral images have complementary characteristics in terms of spectral and spatial resolutions. In this paper we propose an approach for the fusion of low spatial resolution hyperspectral images with high spatial resolution multispectral images in order to obtain superresolution (spatial and spectral) hyperspectral images. The proposed approach is based on the assumption that, since both hyperspectral and multispectral images acquired on the same scene, the corresponding endmembers should be the same. On a first step the hyperspectral image is spectrally downsampled in order to match the multispectral one. Then an endmember extraction algorithm is performed on the downsampled hyperspectral image and the successive abundance estimation is performed on the multispectral one. Finally, the extracted endmembers are up-sampled back to the original hyperspectral space and then used to reconstruct the super-resolution hyperspectral image according to the abundances obtained from the multispectral image
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Hyperspectral remote sensing images (HSIs) usually have high spectral
resolution and low spatial resolution. Conversely, multispectral images (MSIs)
usually have low spectral and high spatial resolutions. The problem of
inferring images which combine the high spectral and high spatial resolutions
of HSIs and MSIs, respectively, is a data fusion problem that has been the
focus of recent active research due to the increasing availability of HSIs and
MSIs retrieved from the same geographical area.
We formulate this problem as the minimization of a convex objective function
containing two quadratic data-fitting terms and an edge-preserving regularizer.
The data-fitting terms account for blur, different resolutions, and additive
noise. The regularizer, a form of vector Total Variation, promotes
piecewise-smooth solutions with discontinuities aligned across the
hyperspectral bands.
The downsampling operator accounting for the different spatial resolutions,
the non-quadratic and non-smooth nature of the regularizer, and the very large
size of the HSI to be estimated lead to a hard optimization problem. We deal
with these difficulties by exploiting the fact that HSIs generally "live" in a
low-dimensional subspace and by tailoring the Split Augmented Lagrangian
Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction
Method of Multipliers (ADMM), to this optimization problem, by means of a
convenient variable splitting. The spatial blur and the spectral linear
operators linked, respectively, with the HSI and MSI acquisition processes are
also estimated, and we obtain an effective algorithm that outperforms the
state-of-the-art, as illustrated in a series of experiments with simulated and
real-life data.Comment: IEEE Trans. Geosci. Remote Sens., to be publishe