BORSOI , Ricardo Augusto

The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EMs), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what...

In many applications, such as brain network connectivity or shopping recommendations, the underlying graph explaining the different interactions between participating agents is unknown. Moreover, many of these interactions may be based on nonlinear relationships, rendering the topology inference problem more complex. This paper presents a new...

In graph signal processing, there are often settings where the graph topology is not known beforehand and has to be estimated from data. Moreover, some graphs can be dynamic, such as brain activity supported by neurons or brain regions. This paper focuses on estimating in an online and adaptive manner a network structure capturing the...

Online detection of abrupt changes in streaming time series is a challenging problem with many applications, in particular when little prior knowledge of the statistics of the data is available and computation resources are scarce. While many algorithms have been developed for Euclidean spaces, there is a wealth of data that belongs to...

Non-parametric detection of change points in streaming time series data that belong to Euclidean spaces has been extensively studied in the literature. Nevertheless, when the data belongs to a Riemannian manifold, existing approaches are no longer applicable as they fail to account for the structure and geometry of the manifold. In this paper,...

Distributed adaptation and learning recently gained considerable attention in solving optimization problems with streaming data collected by multiple agents over a graph. This work focuses on such problems where the solutions lie on a Riemannian manifold. This research topic is of particular interest for many applications, e.g., principal...

Plusieurs méthodes de détection de changements dans des signaux sur graphe ont été proposées dans la littérature. Cependant, si elles exploitent à bon escient la topologie des graphes, elles se limitent au traitement de séries temporelles sur graphe dans des espaces euclidiens. Dans cet article, nous proposons une méthode de détection de...

Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leading approaches to perform spectral unmixing (SU) considering variability of the endmembers (EMs). It represents each EM in the image using libraries of spectral signatures acquired a priori. However, existing spectral libraries are often small and unable to properly capture...

Detecting change points in streaming time series data is a long standing problem in signal processing. A plethora of methods have been proposed to address it, depending on the hypotheses at hand. Non-parametric approaches are particularly interesting as they do not make any assumption on the distribution of data or on the nature of changes....

Hyperspectral and multispectral image fusion (HMIF) allows us to overcome inherent hardware limitations of hyperspectral imaging systems with respect to their lower spatial resolution. However, existing algorithms fail to consider realistic image acquisition conditions, or to leverage the powerful representation capacity of deep neural...

Hyperspectral image (HI) and multispectral image (MI) fusion allows us to overcome the hardware limitations of hyperspectral imaging systems inherent to their lower spatial resolution. Nevertheless, existing algorithms usually fail to consider realistic image acquisition conditions. This article presents a general imaging model that considers...

Multitemporal spectral unmixing (SU) is a powerful tool to process hyperspectral image (HI) sequences due to its ability to reveal the evolution of materials over time and space in a scene. However, significant spectral variability is often observed between collection of images due to variations in acquisition or seasonal conditions. This...

Introducing spatial prior information in hyperspectral imaging (HSI) analysis has led to an overall improvement of the performance of many HSI methods applied for denoising, classification, and unmixing. Extending such methodologies to nonlinear settings is not always straightforward, specially for unmixing problems where the consideration of...

Signal processing methods over graphs and networks have recently been proposed to detect change points occurring in localized communities of nodes. Nevertheless, all these methods are mostly limited to time series data in Euclidean spaces. In this paper, we devise a distributed change point detection method for streaming manifold-valued signals...

Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can greatly benefit from spatial regularization strategies. However, existing spatial regularization strategies lead to...

In this paper we propose a hyperspectral and multispectral image fusion framework accounting for inter-image variability. The images are represented as three dimensional tensors, and both the high-resolution image and inter-image variations are assumed to admit a Tucker decomposition. Two algorithms are proposed, one purely algebraic and...