Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging

Autor: Muzeau, Max, Ren, Chengfang, Angelliaume, Sébastien, Datcu, Mihai, Ovarlez, Jean-Philippe

In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings but without any prior knowledge of their cha

Externí odkaz: http://arxiv.org/abs/2210.16038

Radar Imagery of the Ocean Surface : from Sea State Estimation to Characterization of Marine Pollution

Autor: Angelliaume, Sébastien

Publikováno v: Autre [cond-mat.other]. Université de Toulon, 2020. Français. ⟨NNT : 2020TOUL0021⟩
Sciences de l'ingénieur [physics]. UNIVERSITE DE TOULON, 2020. Français

Radar imagery has been used for more than 40 years for Earth observation. It enables the acquisition of high spatial resolution data, over the entire globe, day and night and in almost all weather conditions. Even though the radar response backscatte

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::5decf4b2873848b9dd0f9937609c3d4a
https://theses.hal.science/tel-03998491/document

Tracking maritime cooperative target with real time motorized SAR antenna

Autor: Castet, Nicolas, Ruault Du Plessis, Olivier, Nouvel, Jean-Francois, Angelliaume, Sébastien

Publikováno v: 2019 International Radar Conference (RADAR 2019)
2019 International Radar Conference (RADAR 2019), Sep 2019, TOULON, France. ⟨10.1109/RADAR41533.2019.171284⟩

International audience; Since 2017, ONERA uses 2-axes motorized antenna to collect SAR images on various geometric acquisitions. Recently, a system has been developed to track maritime targets using the automatic identification system to follow them

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::dec12f72a9c307ba24b7c536e20c9213
https://hal.archives-ouvertes.fr/hal-02470900

Modelling the Amplitude Distribution of Radar Sea Clutter

Autor: Angelliaume, Sébastien, Rosenberg, Luke, Ritchie, Matthew

Publikováno v: Remote Sensing
Remote Sensing, MDPI, 2019, 11 (3), pp.319. ⟨10.3390/rs11030319⟩

International audience; Ship detection in the maritime domain is best performed with radar due to its ability to surveil wide areas and operate in almost any weather condition or time of day. Many common detection schemes require an accurate model of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::947ed30ad6aa584e888e7571093f6436
https://hal.archives-ouvertes.fr/hal-02053295/document

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