595 research outputs found
Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms
We propose a novel approach for hyperspectral super-resolution, that is based
on low-rank tensor approximation for a coupled low-rank multilinear (Tucker)
model. We show that the correct recovery holds for a wide range of multilinear
ranks. For coupled tensor approximation, we propose two SVD-based algorithms
that are simple and fast, but with a performance comparable to the
state-of-the-art methods. The approach is applicable to the case of unknown
spatial degradation and to the pansharpening problem.Comment: IEEE Transactions on Signal Processing, Institute of Electrical and
Electronics Engineers, in Pres
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
In many computer vision applications, obtaining images of high resolution in
both the spatial and spectral domains are equally important. However, due to
hardware limitations, one can only expect to acquire images of high resolution
in either the spatial or spectral domains. This paper focuses on hyperspectral
image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low
spatial resolution (LR) but high spectral resolution is fused with a
multispectral image (MSI) with high spatial resolution (HR) but low spectral
resolution to obtain HR HSI. Existing deep learning-based solutions are all
supervised that would need a large training set and the availability of HR HSI,
which is unrealistic. Here, we make the first attempt to solving the HSI-SR
problem using an unsupervised encoder-decoder architecture that carries the
following uniquenesses. First, it is composed of two encoder-decoder networks,
coupled through a shared decoder, in order to preserve the rich spectral
information from the HSI network. Second, the network encourages the
representations from both modalities to follow a sparse Dirichlet distribution
which naturally incorporates the two physical constraints of HSI and MSI.
Third, the angular difference between representations are minimized in order to
reduce the spectral distortion. We refer to the proposed architecture as
unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results
demonstrate the superior performance of uSDN as compared to the
state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2018, Spotlight
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
The recent advancement of deep learning techniques has made great progress on
hyperspectral image super-resolution (HSI-SR). Yet the development of
unsupervised deep networks remains challenging for this task. To this end, we
propose a novel coupled unmixing network with a cross-attention mechanism,
CUCaNet for short, to enhance the spatial resolution of HSI by means of
higher-spatial-resolution multispectral image (MSI). Inspired by coupled
spectral unmixing, a two-stream convolutional autoencoder framework is taken as
backbone to jointly decompose MS and HS data into a spectrally meaningful basis
and corresponding coefficients. CUCaNet is capable of adaptively learning
spectral and spatial response functions from HS-MS correspondences by enforcing
reasonable consistency assumptions on the networks. Moreover, a cross-attention
module is devised to yield more effective spatial-spectral information transfer
in networks. Extensive experiments are conducted on three widely-used HS-MS
datasets in comparison with state-of-the-art HSI-SR models, demonstrating the
superiority of the CUCaNet in the HSI-SR application. Furthermore, the codes
and datasets will be available at:
https://github.com/danfenghong/ECCV2020_CUCaNet
Nonlinear unmixing of hyperspectral images: Models and algorithms
When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution
This paper focuses on hyperspectral image (HSI) super-resolution that aims to
fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral
image to form a high-spatial-resolution HSI (HR-HSI). Existing deep
learning-based approaches are mostly supervised that rely on a large number of
labeled training samples, which is unrealistic. The commonly used model-based
approaches are unsupervised and flexible but rely on hand-craft priors.
Inspired by the specific properties of model, we make the first attempt to
design a model inspired deep network for HSI super-resolution in an
unsupervised manner. This approach consists of an implicit autoencoder network
built on the target HR-HSI that treats each pixel as an individual sample. The
nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into
the autoencoder network, where the two NMF parts, spectral and spatial
matrices, are treated as decoder parameters and hidden outputs respectively. In
the encoding stage, we present a pixel-wise fusion model to estimate hidden
outputs directly, and then reformulate and unfold the model's algorithm to form
the encoder network. With the specific architecture, the proposed network is
similar to a manifold prior-based model, and can be trained patch by patch
rather than the entire image. Moreover, we propose an additional unsupervised
network to estimate the point spread function and spectral response function.
Experimental results conducted on both synthetic and real datasets demonstrate
the effectiveness of the proposed approach
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