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Seafloor characterization for ATR applications using the monogenic signal and the intrinsic dimensionality

Laurent Picard 1, 2 Alexandre Baussard 1, 2 Gilles Le Chenadec 3, 4 Isabelle Quidu 3, 4
1 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 Pôle STIC_REMS
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
3 Pôle STIC_OSM
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
4 Lab-STICC_ENSTAB_CID_PRASYS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : In mine warfare context, environmental effects are known to degrade performances of most of automatic target recognition (ATR) processes. In this study, we consider the environment as an information that can be used to design a robust ATR process. Hence, we investigate a way to extract and exploit information about the seafloor using an isotropic analysis of sidescan sonar images based on the monogenic signal. This tool provides an orthogonal separation between energetic, geometrical and structural information of the 2D signal in a scale-space framework. It also allows to efficiently compute the continuous intrinsic dimensionality scale-space. We propose to use these last descriptors to characterize the sidescan sonar images in terms of homogeneous, anisotropic and complex areas. In each of these areas it can be expected that adapted ATR processes could be defined to outperform classical global approaches.
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-01466835
Contributeur : Annick Billon-Coat <>
Soumis le : lundi 13 février 2017 - 18:00:28
Dernière modification le : mercredi 24 juin 2020 - 16:19:51

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Laurent Picard, Alexandre Baussard, Gilles Le Chenadec, Isabelle Quidu. Seafloor characterization for ATR applications using the monogenic signal and the intrinsic dimensionality. OCEANS 2016 MTS/IEEE Monterey, Sep 2016, Monterey, United States. ⟨10.1109/OCEANS.2016.7761372⟩. ⟨hal-01466835⟩

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