3,850 research outputs found
Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation
International audienceThe Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a tree structure. BPT allows users to explore the image at different segmentation scales. Often, the tree is pruned to get a more compact representation and so the remaining nodes conform an optimal partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear Spectral Unmixing (LSU) consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions
Development and testing of improved spectral unmixing techniques
The tradeoff between spatial and spectral resolution gives rise to a finite ground sample size, which produces data with spectrally mixed pixels in a multispectral and hyperspectral systems. Assuming the mixed pixel to be a linear combination of pure spectra known as endmembers, the fractional abundance of the endmembers can be calculated by linear spectral unmixing. Constraints may be placed to force the unmixed fractions to behave realistically. There are also different methods of selecting these endmember spectra, including scene derived and laboratory measured spectra. The stepwise unmixing uses an iterative regression technique to select the optimal endmembers on a per pixel basis. It shows promise of improving the unmixing process by introducing flexibility in endmember selection. However, stepwise procedure has not been rigorously tested, and its parameters are not well characterized. It was the aim of this research to characterize the major parameters of stepwise unmixing, including spectral resolution, library size, F-to-enter/exit, and pixel mixture complexity. In addition to the parametric studies, a comparison between stepwise procedure and hierarchical unmixing was made, as well as applications of unmixing algorithms to non-remote sensing data such as nuclear magnetic resonance (NMR) spectra. The parametric studies were conducted on synthetic data for its advantages of knowing exactly what is in the mixture. The algorithm was also tested with images to confirm the results of the parametric studies. The result showed that the stepwise procedure is capable of results comparable with the best traditional unmixing case
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Nonnegative matrix factorization (NMF) is a powerful class of feature
extraction techniques that has been successfully applied in many fields, namely
in signal and image processing. Current NMF techniques have been limited to a
single-objective problem in either its linear or nonlinear kernel-based
formulation. In this paper, we propose to revisit the NMF as a multi-objective
problem, in particular a bi-objective one, where the objective functions
defined in both input and feature spaces are taken into account. By taking the
advantage of the sum-weighted method from the literature of multi-objective
optimization, the proposed bi-objective NMF determines a set of nondominated,
Pareto optimal, solutions instead of a single optimal decomposition. Moreover,
the corresponding Pareto front is studied and approximated. Experimental
results on unmixing real hyperspectral images confirm the efficiency of the
proposed bi-objective NMF compared with the state-of-the-art methods
Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
Light propagating in tissue attains a spectrum that varies with location due
to wavelength-dependent fluence attenuation by tissue optical properties, an
effect that causes spectral corruption. Predictions of the spectral variations
of light fluence in tissue are challenging since the spatial distribution of
optical properties in tissue cannot be resolved in high resolution or with high
accuracy by current methods. Spectral corruption has fundamentally limited the
quantification accuracy of optical and optoacoustic methods and impeded the
long sought-after goal of imaging blood oxygen saturation (sO2) deep in
tissues; a critical but still unattainable target for the assessment of
oxygenation in physiological processes and disease. We discover a new principle
underlying light fluence in tissues, which describes the wavelength dependence
of light fluence as an affine function of a few reference base spectra,
independently of the specific distribution of tissue optical properties. This
finding enables the introduction of a previously undocumented concept termed
eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively
account for wavelength dependent light attenuation without explicit knowledge
of the tissue optical properties. We validate eMSOT in more than 2000
simulations and with phantom and animal measurements. We find that eMSOT can
quantitatively image tissue sO2 reaching in many occasions a better than
10-fold improved accuracy over conventional spectral optoacoustic methods.
Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor;
revealing so far unattainable tissue physiology patterns. Last, we related
eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2
images and tissue perfusion and hypoxia maps obtained by correlative
histological analysis
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
In hyperspectral images, some spectral bands suffer from low signal-to-noise
ratio due to noisy acquisition and atmospheric effects, thus requiring robust
techniques for the unmixing problem. This paper presents a robust supervised
spectral unmixing approach for hyperspectral images. The robustness is achieved
by writing the unmixing problem as the maximization of the correntropy
criterion subject to the most commonly used constraints. Two unmixing problems
are derived: the first problem considers the fully-constrained unmixing, with
both the non-negativity and sum-to-one constraints, while the second one deals
with the non-negativity and the sparsity-promoting of the abundances. The
corresponding optimization problems are solved efficiently using an alternating
direction method of multipliers (ADMM) approach. Experiments on synthetic and
real hyperspectral images validate the performance of the proposed algorithms
for different scenarios, demonstrating that the correntropy-based unmixing is
robust to outlier bands.Comment: 23 page
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