AIS-based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance

International audienceThis paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detec- tion algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (Automatic Identification System) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels' length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations

A Multibranch Convolutional Neural Network for Hyperspectral Unmixing

Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic unmixing techniques, and can be effectively deployed onboard Earth observation satellites equipped with hyperspectral imagers. In this letter, we follow this research pathway and propose a multi-branch convolutional neural network that benefits from fusing spectral, spatial, and spectral-spatial features in the unmixing process. The results of our experiments, backed up with the ablation study, revealed that our techniques outperform others from the literature and lead to higher-quality fractional abundance estimation. Also, we investigated the influence of reducing the training sets on the capabilities of all algorithms and their robustness against noise, as capturing large and representative ground-truth sets is time-consuming and costly in practice, especially in emerging Earth observation scenarios.Comment: 14 pages (including supplementary material), published in IEEE Geoscience and Remote Sensing Letter

Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series

Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral information concerning the scanned area, hence allows us to benefit from subtle spectral features during the analysis process in agricultural applications. We introduce an approach for extracting 2.5 m cultivated land maps from 10 m Sentinel-2 multispectral image series which benefits from a compact graph convolutional neural network. The experiments indicate that our models not only outperform classical and deep machine learning techniques through delivering higher-quality segmentation maps, but also dramatically reduce the memory footprint when compared to U-Nets (almost 8k trainable parameters of our models, with up to 31M parameters of U-Nets). Such memory frugality is pivotal in the missions which allow us to uplink a model to the AI-powered satellite once it is in orbit, as sending large nets is impossible due to the time constraints.Comment: 7 pages (including supplementary material), published in IEEE Geoscience and Remote Sensing Letter

A Return on Our Experience of Modeling a Service-oriented Organization in a Service Cartography

We present a longitudinal project using action design research, which is a four-year collaboration between two EPFL entities: The research Laboratory for Systemic Modeling (LAMS) and EPFL’s IT department, called the VPSI. During that time the VPSI was going through a transformation into a service-oriented organization. The research project began as an open-ended modeling of some of the VPSI processes. It slowly matured into the design and development of a visualization tool we call service cartography. During this research, we learned that, to successfully apply service-orientation, focusing purely on IT architecture and end-customer value is not enough. Attention must be given to the exchange of internal services between the service organization members and their alignment with the services expected by the external stakeholders. In this paper we present the evolution of (1) our understanding of what services are, and (2) our conceptualization of how the service cartography facilitates the service-oriented thinking

Assimilation variationnelle de données dans le modèle d'interface sol – végétation - atmosphère ISBA.

Rapport de stage de fin d'études de l'INSA-Rennes, Septembre 2004, rapport interne CET

Apport de l'Imagerie SAR Satellitaire en Bandes L et C pour la Caractérisation du Couvert Neigeux.

This thesis concerns snow remote sensing using spaceborne SAR imagery at L- and CBands.An electromagnetic (EM) backscattering model is developed to calculate radar backscatter from snow cover. This model takes into consideration both the vertical snowpack structure and the metamorphosis state of each snow layer. It is validated using in situsnow profiles and SAR data simultaneously acquired by the ASAR/ENVISAT sensor in 2004.The main contribution of this study consists in the combination of dual-polarization SAR data with the meteorological Crocus model developed by Météo-France. To characterize the variability of alpine snowpack, Crocus snow profiles are spatially reorganized by minimizing the difference between simulated and measured C-Band SAR data. Snowcharacteristics maps have been created at SAR resolution level for the French massifs "Grandes Rousses" and "Oisans". The potential of polarimetric L-Band SAR data for snow characterization is investigated in rural areas. A classification method based on Support Vector Machine techniques is developed and evaluated with SAR data acquired by the PALSAR/ALOS sensor.Cette thèse traite de l'apport de l'imagerie SAR satellitaire en bandes L et C pour lacaractérisation du couvert neigeux. Un modèle électromagnétique (EM) permettant de simuler la rétrodiffusion de l'ondesur un couvert neigeux a été développé. Ce modèle prend en considération la structure verticale du manteau neigeux ainsi que l'état de métamorphose des différentes couches. Il est validé à l'aide de profils stratigraphiques mesurés et des données SAR acquisesparallèlement par le capteur ASAR/ENVISAT en 2004.L'originalité principale de cette étude consiste en l'association des données SAR à polarisation double avec le modèle météorologique Crocus développé par Météo-France.Dans le but de caractériser la variabilité spatiale des couverts neigeux alpins, les profils stratigraphiques Crocus sont réorganisés spatialement par le biais d'une optimisation de la réponse EM en bande C. Des cartographies du couvert neigeux sont réalisées avec une résolution métrique pour les massifs alpins des Grandes Rousses et de l'Oisans.Finalement, le potentiel des données polarimétriques en bande L pour la caractérisation de la neige est étudié sur des zones rurales. Une méthode de cartographie basée sur les Machines à Vecteurs Supports est réalisée puis testée avec des données acquises par lecapteur PALSAR/ALOS

Toward an operational method for refined characterization using dual-Polarization C-Band SAR data.

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