Article Dans Une Revue IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Année : 2025

Classification of Buried Objects From Ground Penetrating Radar Images by Using Second-Order Deep Learning Models

Douba Jafuno
  • Fonction : Auteur
Guillaume Ginolhac
Nickolas Stelzenmuller
  • Fonction : Auteur

Résumé

In this paper, a new classification model based on covariance matrices is built in order to classify buried objects. The inputs of the proposed models are the hyperbola thumbnails obtained with a classical Ground Penetrating Radar (GPR) system. These thumbnails are then inputs to the first layers of a classical CNN, which then produces a covariance matrix using the outputs of the convolutional filters. Next, the covariance matrix is given to a network composed of specific layers to classify Symmetric Positive Definite (SPD) matrices. We show in a large database that our approach outperform shallow networks designed for GPR data and conventional CNNs typically used in computer vision applications, particularly when the number of training data decreases and in the presence of mislabeled data. We also illustrate the interest of our models when training data and test sets are obtained from different weather modes or considerations.
Fichier principal
Vignette du fichier
main.pdf (1.9 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04902720 , version 1 (21-01-2025)

Identifiants

Citer

Douba Jafuno, Ammar Mian, Guillaume Ginolhac, Nickolas Stelzenmuller. Classification of Buried Objects From Ground Penetrating Radar Images by Using Second-Order Deep Learning Models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, pp.3185-3197. ⟨10.1109/JSTARS.2024.3524424⟩. ⟨hal-04902720⟩

Collections

UNIV-SAVOIE LISTIC
0 Consultations
0 Téléchargements

Altmetric

Partager

More