BORSOI , Ricardo

Identifying directed connectivity patterns from nodal measurements is an important problem in network analysis. Recent works proposed to leverage the performance and flexibility of strategies operating in reproducing kernel Hilbert spaces (RKHS) to model nonlinear interactions between network agents. Moreover, several applications require...

In many areas such as computational biology, finance or social sciences, knowledge of an underlying graph explaining the interactions between agents is of paramount importance but still challenging. Considering that these interactions may be based on nonlinear relationships adds further complexity to the topology inference problem. Among the...

The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large...

Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with strong theoretical guarantees. However, tensor-based approaches previously proposed assume that the different observed images are acquired under exactly the same...

Nous proposons une solution conjointe aux problèmes de super-résolution et de démélange de l'image super-résolue. Cette approche utilise la décomposition tensorielle LL1 et tient compte d'un phénomène de variabilité spectrale. Des garanties théoriques de reconstruction sont fournies. Nous proposons un algorithme sous contraintes de positivité,...

International audience

Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images (respectively HSI and MSI). This problem is referred to as hyperspectral super-resolution, and consists in recovering a super-resolution image (SRI). Previously proposed tensor-based approaches share a common...

In this paper, we propose to jointly solve the hyperspectral super-resolution problem and the unmixing problem of the underlying super-resolution image using a coupled LL1 block-tensor decomposition. We consider a spectral variability phenomenon occurring between the observed low-resolution images. Exact recovery conditions for the image and...

The final version of this paper can be found in the IEEE Geoscience and Remote Sensing Magazine. 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....