215 research outputs found
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure
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
Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion
In multiband fusion, an image with a high spatial and low spectral resolution
is combined with an image with a low spatial but high spectral resolution to
produce a single multiband image having high spatial and spectral resolutions.
This comes up in remote sensing applications such as pansharpening~(MS+PAN),
hyperspectral sharpening~(HS+PAN), and HS-MS fusion~(HS+MS). Remote sensing
images are textured and have repetitive structures. Motivated by nonlocal
patch-based methods for image restoration, we propose a convex regularizer that
(i) takes into account long-distance correlations, (ii) penalizes patch
variation, which is more effective than pixel variation for capturing texture
information, and (iii) uses the higher spatial resolution image as a guide
image for weight computation. We come up with an efficient ADMM algorithm for
optimizing the regularizer along with a standard least-squares loss function
derived from the imaging model. The novelty of our algorithm is that by
expressing patch variation as filtering operations and by judiciously splitting
the original variables and introducing latent variables, we are able to solve
the ADMM subproblems efficiently using FFT-based convolution and
soft-thresholding. As far as the reconstruction quality is concerned, our
method is shown to outperform state-of-the-art variational and deep learning
techniques.Comment: Accepted in IEEE Transactions on Computational Imagin
Fusing Multiple Multiband Images
We consider the problem of fusing an arbitrary number of multiband, i.e.,
panchromatic, multispectral, or hyperspectral, images belonging to the same
scene. We use the well-known forward observation and linear mixture models with
Gaussian perturbations to formulate the maximum-likelihood estimator of the
endmember abundance matrix of the fused image. We calculate the Fisher
information matrix for this estimator and examine the conditions for the
uniqueness of the estimator. We use a vector total-variation penalty term
together with nonnegativity and sum-to-one constraints on the endmember
abundances to regularize the derived maximum-likelihood estimation problem. The
regularization facilitates exploiting the prior knowledge that natural images
are mostly composed of piecewise smooth regions with limited abrupt changes,
i.e., edges, as well as coping with potential ill-posedness of the fusion
problem. We solve the resultant convex optimization problem using the
alternating direction method of multipliers. We utilize the circular
convolution theorem in conjunction with the fast Fourier transform to alleviate
the computational complexity of the proposed algorithm. Experiments with
multiband images constructed from real hyperspectral datasets reveal the
superior performance of the proposed algorithm in comparison with the
state-of-the-art algorithms, which need to be used in tandem to fuse more than
two multiband images
Robust fusion of multi-band images with different spatial and spectral resolutions for change detection
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through different kinds of sensors. More precisely, this paper addresses the problem of detecting changes between two multiband optical images characterized by different spatial and spectral resolutions. This sensor dissimilarity introduces additional issues in the context of operational change detection. To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e.g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images. Nevertheless, these preprocessing steps tend to throw away relevant information. Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatial and spectral versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. As they cover the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a robust multiband image fusion method, which enforces the differences between the estimated latent images to be spatially sparse. This robust fusion problem is formulated as an inverse problem, which is iteratively solved using an efficient block-coordinate descent algorithm. The proposed method is applied to real panchromatic, multispectral, and hyperspectral images with simulated realistic and real changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy
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