mlsanityData to 14 Oct 2026

Classification of Buried Objects from Ground Penetrating Radar Images by using Second Order Deep Learning Models

arXiv 2410.07117 ↗First posted 20 Sep 2024cs.CV cs.LG stat.APcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 62 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

18 of 62 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2015Fast R-CNNcited by 2,933
  4. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  5. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
  6. 2016A Riemannian Network for SPD Matrix Learningcited by 162
  7. 2019Riemannian batch normalization for SPD neural networkscited by 72
  8. 2017Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalizationcited by 65
  9. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  10. 2017Is Second-order Information Helpful for Large-scale Visual Recognition?cited by 44
  11. 2015Training Deep Networks with Structured Layers by Matrix Backpropagationcited by 30
  12. 2019GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Liningcited by 4
6 more
  1. 2022Riemannian optimization for non-centered mixture of scaled Gaussian distributionscited by 4
  2. 2022Robust Geometric Metric Learningcited by 3
  3. 2018Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Datacited by 2
  4. 2018Dictionary Learning for Adaptive GPR Landmine Classificationcited by 1
  5. 2022DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditionscited by 1
  6. 2023Riemannian Complex Matrix Convolution Network for PolSAR Image Classificationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnetcited by 2
  2. 2026An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusioncited by 0
  3. 2026Unsupervised Detection of Underground Tunnels in Ground-Penetrating Radar Using Depth-Restricted Reconstruction Scoringcited by 0