Search Results for author:
Found 34 papers, 6 papers with code
Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing
In response, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution.
Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices.
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.
Adaptive Random Fourier Features Kernel LMS
We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS).
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing.
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention.
Transient Performance Analysis of the $\ell_1$-RLS
The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems.
Graph topology inference with derivative-reproducing property in RKHS: algorithm and convergence analysis
Contrasting with previous approaches based on linear models, the considered model is able to explain nonlinear interactions between the agents in a network.
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.
Transient Theoretical Analysis of Diffusion RLS Algorithm for Cyclostationary Colored Inputs
Convergence of the diffusion RLS (DRLS) algorithm to steady-state has been extensively studied in the literature, whereas no analysis of its transient convergence behavior has been reported yet.
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and {the} learned prior to reconstruct the final HR HSI.
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.
Online change-point detection with kernels
Change-points in time series data are usually defined as the time instants at which changes in their properties occur.
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).
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.
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.
On reducing the communication cost of the diffusion LMS algorithm
While these data may be processed in a centralized manner, it is often more suitable to consider distributed strategies such as diffusion as they are scalable and can handle large amounts of data by distributing tasks over networked agents.
Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed environments, e. g., sensor networks or peer-to-peer protocols.
Multitask diffusion adaptation over networks with common latent representations
Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications.
Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem
Kernel-based nonlinear mixing models have been applied to unmix spectral information of hyperspectral images when the type of mixing occurring in the scene is too complex or unknown.
Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning
Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system.
Distributed image reconstruction for very large arrays in radio astronomy
Current and future radio interferometric arrays such as LOFAR and SKA are characterized by a paradox.
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images
The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model.
A graph Laplacian regularization for hyperspectral data unmixing
This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation.
Blind and fully constrained unmixing of hyperspectral images
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images.
Steady-state performance of non-negative least-mean-square algorithm and its variants
The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in our previous work.
Convergence analysis of kernel LMS algorithm with pre-tuned dictionary
In addition to choosing a reproducing kernel and setting filter parameters, designing a KLMS adaptive filter requires to select a so-called dictionary in order to get a finite-order model.
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes.
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm
Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data.
Distributed dictionary learning over a sensor network
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements.
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).
