RICHARD , Cédric

We consider the problem of distributed dictionary learning, where a set of nodes is required to collec- tively learn a common dictionary from noisy measure- ments. This approach may be useful in several con- texts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In...

In this paper, we study the problem of decomposing spectra in hyperspectral data into the sum of pure spectra, or endmembers. We propose to jointly extract the endmembers and estimate the corresponding fractions, or abundances. For this purpose, we show that these abundances can be easily computed using volume of simplices, from the same...

We consider the problem of distributed dictionary learning, where a set of nodes is required to collec- tively learn a common dictionary from noisy measure- ments. This approach may be useful in several con- texts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In...

De nombreuses études ont récemment montré l'avantage de l'approche géométrique en démélange de données hyperspectrales. Elle permet d'identifier les signatures spectrales des composants purs. Jusqu'ici, l'estimation des fractions d'abondance a toujours été réalisée dans un second temps, par résolution d'un problème inverse généralement. Dans...

The pre-image problem is a challenging research subject pursued by many researchers in machine learning. Kernel-based machines seek some relevant feature in a reproducing kernel Hilbert space (RKHS), optimized in a given sense, such as kernel-PCA algorithms. Operating the latter for denoising requires solving the pre-image problem, i.e....

In hyperspectral imaging, spectral unmixing is one of the most challenging and fundamental problems. It consists of breaking down the spectrum of a mixed pixel into a set of pure spectra, called endmembers, and their contributions, called abundances. Many endmember extraction techniques have been proposed in literature, based on either a...

We consider the problem of a set of nodes which is required to collectively learn a common dictionary from noisy measurements. This distributed dictionary learning approach may be useful in several contexts including sensor networks. Dif-fusion cooperation schemes have been proposed to estimate a consensus solution to distributed linear...

Support vector machines have been investigated with success for hyperspectral data classification. In this paper, we propose a new kernel to measure spectral similarity, called the angular kernel. We provide some of its properties, such as its invariance to illumination energy, as well as connection to previous work. Furthermore, we show that...

While the nonlinear mapping from the input space to the feature space is central in kernel methods, the reverse mapping from the feature space back to the input space is also of primary interest. This is the case in many applications, including kernel principal component analysis (PCA) for signal and image denoising. Unfortunately, it turns out...

We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In this...

We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In this...

Detecting changes in network-structured time series data is of utmost importance in critical applications as diverse as detecting denial of service attacks against online service providers or monitoring energy and water supplies. The aim of this paper is to address this challenge when anomalies activate unknown groups of nodes in a network. We...

Hyperspectral data unmixing has attracted considerable attention in recent years. Hyperspectral data may however suffer from varying levels of signal-to-noise ratio over spectral bands. In this paper, we investigate a robust approach for nonlinear hyperspectral data unmixing. Each observed pixel is modeled as a linear mixing of endmember...

International audience

This communication proposes an unsupervised neighbor dependent nonlinear unmixing algorithm for hyperspectral data. The proposed mixing scheme models the reflectance vector of a pixel as the sum of a linear combination of the endmembers plus a nonlinear function acting on neighboring spectra. The nonlinear function belongs to a reproducing...

The invention relates to a system and a method for locating at least one target using au array of transceivers or sensors, in which at least a portion has a known geographic location, each comprising data processing means implementing at least one algorithm for locating a target, means for transmitting/receiving a signal that decreases with the...

In this paper, we investigate a novel online one-class classification method. We consider a least-squares optimization problem, where the model complexity is controlled by the coherence criterion as a sparsification rule. This criterion is coupled with a simple updating rule for online learning, which yields a low computational demanding...

International audience

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images (HSIs) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a...

The estimation of fractional abundances under physical constraints is a fundamental problem in hyperspectral data processing. In this paper, we propose to adapt Kaczmarz's cyclic projections to solve this problem. The main contribution of this work is two-fold: On the one hand, we show that the non-negativity and the sum-to-one constraints can...