Mauro Dalla Mura

In Proceedings of the 31th european signal processing conference (EUSIPCO)

Abstract Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth’s surface have been made publicly available for scientific purpose, for example through the European Copernicus project. Simultaneously, the development of self-supervised learning (SSL) methods has sparked great interest in the remote sensing community, enabling to learn latent representations from unlabeled data to help treating downstream tasks for which there is few annotated examples, such as interpolation, forecasting or unmixing.

Leveraging neural koopman operators to learn continuous representations of dynamical systems from scarce data

By Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aı̈ssa-El-Bey

2023-02-17

In Proceedings of the 48th IEEE international conference on acoustics, speech, and signal processing (ICASSP)

Abstract Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data.

Assimilation de données variationnelle de séries temporelles d’images sentinel-2 avec un modèle dynamique auto-supervisé

By Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aı̈ssa-El-Bey

2022-06-14

In 29e colloque sur le traitement du signal et des images

Abstract Au cours des dernières années, l’apprentissage profond a acquis une importance croissante dans de nombreux domaines scientifiques, notamment en ce qui concerne le traitement d’images, et en particulier pour le traitement des données issues de satellites. Le paradigme le plus courant en ce qui concerne l’apprentissage profond est l’apprentissage supervisé, qui requiert une grande quantité de données annotées représentant la vérité terrain pour la tâche d’intérêt. Or, obtenir des données correctement annotées pose souvent des difficultés financières ou techniques importantes.

Learning endmember dynamics in multitemporal hyperspectral data using a state-space model formulation

By Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Ronan Fablet

2020-01-24

In Proceedings of the 45th IEEE international conference on acoustics, speech, and signal processing (ICASSP)

Abstract Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (called abundances) in every pixel of the image. However, in spite of a tremendous applicative potential and the avent of new satellite sensors with high temporal resolution, multitemporal hyperspectral unmixing is still a relatively underexplored research avenue in the community, compared to standard image unmixing. In this paper, we propose a new framework for multitemporal unmixing and endmember extraction based on a state-space model, and present a proof of concept on simulated data to show how this representation can be used to inform multitemporal unmixing with external prior knowledge, or on the contrary to learn the dynamics of the quantities involved from data using neural network architectures adapted to the identification of dynamical systems.

Braids of partitions for the hierarchical representation and segmentation of multimodal images

By Guillaume Tochon, Mauro Dalla Mura, Miguel Angel Veganzones, Thierry Géraud, Jocelyn Chanussot

2019-07-01

In Pattern Recognition

Abstract Hierarchical data representations are powerful tools to analyze images and have found numerous applications in image processing. When it comes to multimodal images however, the fusion of multiple hierarchies remains an open question. Recently, the concept of braids of partitions has been proposed as a theoretical tool and possible solution to this issue. In this paper, we demonstrate the relevance of the braid structure for the hierarchical representation of multimodal images.

Constructing a braid of partitions from hierarchies of partitions

By Guillaume Tochon, Mauro Dalla Mura, Jocelyn Chanussot

2019-03-13

In Mathematical morphology and its application to signal and image processing – proceedings of the 14th international symposium on mathematical morphology (ISMM)

Abstract Braids of partitions have been introduced in a theoretical framework as a generalization of hierarchies of partitions, but practical guidelines to derive such structures remained an open question. In a previous work, we proposed a methodology to build a braid of partitions by experimentally composing cuts extracted from two hierarchies of partitions, notably paving the way for the hierarchical representation of multimodal images. However, we did not provide the formal proof that our proposed methodology was yielding a braid structure.

Advances in utilization of hierarchical representations in remote sensing data analysis

By Guillaume Tochon, Mauro Dalla Mura, Miguel-Angel Veganzones, Silvia Valero, Philippe Salembier, Jocelyn Chanussot

2017-11-08

In Comprehensive remote sensing, 1st edition

Abstract The latest developments in sensor design for remote sensing and Earth observation purposes are leading to images always more complex to analyze. Low-level pixel-based processing is becoming unadapted to efficiently handle the wealth of information they contain, and higher levels of abstraction are required. Region-based representations intend to exploit images as collections of regions of interest bearing some semantic meaning, thus easing their interpretation. However, the scale of analysis of the images has to be fixed beforehand, which can be problematic as different applications may not require the same scale of analysis.

Object tracking by hierarchical decomposition of hyperspectral video sequences: Application to chemical gas plume tracking

By Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea Bertozzi

2017-04-20

In IEEE Transactions on Geoscience and Remote Sensing

Abstract It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this article, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking.

Region-based classification of remote sensing images with the morphological tree of shapes

By Gabriele Cavallaro, Mauro Dalla Mura, Edwin Carlinet, Thierry Géraud, Nicola Falco, Jón Atli Benediktsson

2016-04-12

In Proceedings of the IEEE international geoscience and remote sensing symposium (IGARSS)

Abstract Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approach, the underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image.

Learning sentinel-2 reflectance dynamics for data-driven assimilation and forecasting

Leveraging neural koopman operators to learn continuous representations of dynamical systems from scarce data

Assimilation de données variationnelle de séries temporelles d’images sentinel-2 avec un modèle dynamique auto-supervisé

Learning endmember dynamics in multitemporal hyperspectral data using a state-space model formulation

Braids of partitions for the hierarchical representation and segmentation of multimodal images

Constructing a braid of partitions from hierarchies of partitions

Advances in utilization of hierarchical representations in remote sensing data analysis

Object tracking by hierarchical decomposition of hyperspectral video sequences: Application to chemical gas plume tracking

Region-based classification of remote sensing images with the morphological tree of shapes

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