no code implementations • 7 Dec 2022 • Siyuan Yuan, Martijn van den Ende, Jingxiao Liu, Hae Young Noh, Robert Clapp, Cédric Richard, Biondo Biondi
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.
1 code implementation • 28 Nov 2022 • Xiuheng Wang, Jie Chen, Cédric Richard
Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices.
1 code implementation • 24 Aug 2022 • Xiuheng Wang, Ricardo Augusto Borsoi, Cédric Richard, Jie Chen
The fusion problem is stated as an optimization problem in the maximum a posteriori framework.
no code implementations • 14 Jul 2022 • Wei Gao, Jie Chen, Cédric Richard, Wentao Shi, Qunfei Zhang
We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS).
1 code implementation • 11 Jun 2022 • Jie Chen, Min Zhao, Xiuheng Wang, Cédric Richard, Susanto Rahardja
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing.
1 code implementation • 24 Jan 2022 • Xiuheng Wang, Jie Chen, Cédric Richard
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.
no code implementations • 14 Sep 2021 • Wei Gao, Jie Chen, Cédric Richard, Wentao Shi, Qunfei Zhang
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.
no code implementations • 28 Apr 2021 • Mircea Moscu, Ricardo A. Borsoi, Cédric Richard, José-Carlos M. Bermudez
Contrasting with previous approaches based on linear models, the considered model is able to explain nonlinear interactions between the agents in a network.
no code implementations • 7 Apr 2021 • Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
However, MESMA does not consider the relationship between the different HIs, and its computational complexity is extremely high for large spectral libraries.
no code implementations • 12 Jan 2021 • Wei Gao, Jie Chen, Cédric Richard
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.
1 code implementation • 9 Sep 2020 • Xiuheng Wang, Jie Chen, Qi Wei, Cédric Richard
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.
no code implementations • 30 Jun 2020 • Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich, David Brie, José Carlos Moreira Bermudez, Cédric Richard
In this paper, we consider the image fusion problem while accounting for both spatially and spectrally localized changes in an additive model.
no code implementations • 24 Jun 2020 • Ricardo Augusto Borsoi, Cédric Richard, André Ferrari, Jie Chen, José Carlos Moreira Bermudez
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.
no code implementations • 7 Feb 2020 • André Ferrari, Cédric Richard, Anthony Bourrier, Ikram Bouchikhi
Change-points in time series data are usually defined as the time instants at which changes in their properties occur.
1 code implementation • 21 Jan 2020 • Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jocelyn Chanussot, Lucas. Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten
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.
no code implementations • 2 Jan 2020 • Ricardo Augusto Borsoi, Tales Imbiriba, Pau Closas, José Carlos Moreira Bermudez, Cédric Richard
The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images.
no code implementations • 20 Sep 2019 • Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leading approaches to perform spectral unmixing (SU) considering variability of the endmembers (EMs).
no code implementations • 19 Aug 2019 • Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
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.
no code implementations • 5 Dec 2017 • Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications.
no code implementations • 30 Nov 2017 • Ibrahim El Khalil Harrane, Rémi Flamary, Cédric Richard
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.
no code implementations • 28 Apr 2017 • Simone Scardapane, Jie Chen, Cédric Richard
In this chapter, we analyze nonlinear filtering problems in distributed environments, e. g., sensor networks or peer-to-peer protocols.
no code implementations • 13 Feb 2017 • Jie Chen, Cédric Richard, Ali H. Sayed
Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications.
no code implementations • 1 Mar 2016 • Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
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.
no code implementations • 24 Aug 2015 • Jingen Ni, Jian Yang, Jie Chen, Cédric Richard, José Carlos M. Bermudez
Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system.
no code implementations • 2 Jul 2015 • André Ferrari, David Mary, Rémi Flamary, Cédric Richard
Current and future radio interferometric arrays such as LOFAR and SKA are characterized by a paradox.
no code implementations • 18 Mar 2015 • Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jean-Yves Tourneret
The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model.
no code implementations • 14 Oct 2014 • Rita Ammanouil, André Ferrari, Cédric Richard
This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation.
no code implementations • 3 Mar 2014 • Rita Ammanouil, André Ferrari, Cédric Richard, David Mary
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images.
no code implementations • 24 Jan 2014 • Jie Chen, José Carlos M. Bermudez, Cédric Richard
The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in our previous work.
no code implementations • 31 Oct 2013 • Jie Chen, Wei Gao, Cédric Richard, Jose-Carlos M. Bermudez
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.
no code implementations • 31 Oct 2013 • Jie Chen, Cédric Richard, Alfred O. Hero III
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes.
no code implementations • 22 Jun 2013 • Wei Gao, Jie Chen, Cédric Richard, Jianguo Huang
Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data.
no code implementations • 12 Apr 2013 • Pierre Chainais, Cédric Richard
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements.
no code implementations • 6 Apr 2013 • Nicolas Dobigeon, Jean-Yves Tourneret, Cédric Richard, José C. M. Bermudez, Stephen McLaughlin, Alfred O. Hero
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).