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Found 27 papers, 5 papers with code
Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms
A flexible model is proposed where each dataset is represented as the sum of two components, one related to a common tensor through a multilinear measurement model, and another specific to each dataset.
Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis
Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data.
Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
The proposed framework is capable of producing fast and accurate predictions over long time horizons, dealing with irregularly sampled noisy measurements to correct the solution, and benefits from the decoupling between the spatial and temporal dynamics of this class of PDEs.
A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm
In hyperspectral sparse unmixing, a successful approach employs spectral bundles to address the variability of the endmembers in the spatial domain.
On the Impact of Sampling on Deep Sequential State Estimation
In this paper, importance sampling is applied to the DKF framework for learning deep Markov models, resulting in the IW-DKF, which shows an improvement in terms of log-likelihood estimates and KL divergence between the variational distribution and the transition model.
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques.
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process.
Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data.
Deep Hyperspectral and Multispectral Image Fusion with Inter-image Variability
The fusion problem is stated as an optimization problem in the maximum a posteriori framework.
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abundances, the decoder with the mixing model and the encoder with its inverse.
Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data
However, MESMA does not consider the relationship between the different HIs, and its computational complexity is extremely high for large spectral libraries.
Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
In this paper, we consider the image fusion problem while accounting for both spatially and spectrally localized changes in an additive model.
Online Graph-Based Change Point Detection in Multiband Image Sequences
To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes.
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.
Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images.
Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leading approaches to perform spectral unmixing (SU) considering variability of the endmembers (EMs).
Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme
Although recent progress in video SRR has significantly improved the quality of the reconstructed HR sequences, it remains challenging to design SRR algorithms that achieve good quality and robustness at a small computational complexity, being thus suitable for online applications.
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
Furthermore, we employ a theory-based statistical framework to devise a consistent strategy to estimate all required parameters, including both the regularization parameters of the algorithm and the number of superpixels of the transformation, resulting in a truly blind (from the parameters setting perspective) unmixing method.
Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model.
Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor
Afterwards, we solve a matrix-factorization problem to estimate the fractional abundances using the variability scaling factors estimated in the previous step, what leads to a significantly more well-posed problem.
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability
Recently, tensor-based strategies considered low-rank decompositions of hyperspectral images as an alternative to impose low-dimensional structures on the solutions of standard and multitemporal unmixing problems.
Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability
This paper introduces a novel HS-MS image fusion strategy that combines an unmixing-based formulation with an explicit parametric model for typical spectral variability between the two images.
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes.
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
Tensor-based methods have recently emerged as a more natural and effective formulation to address many problems in hyperspectral imaging.
Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications.
Generalized linear mixing model accounting for endmember variability
Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images.
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations
In this paper, a new video super-resolution reconstruction (SRR) method with improved robustness to outliers is proposed.
