A Comparison of Feature Representations for Explosive Threat Detection in Ground Penetrating Radar Data-Reference-Cited by-同舟云学术

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A Comparison of Feature Representations for Explosive Threat Detection in Ground Penetrating Radar Data

Published:2017-12 Issue:12 Volume:55 Page:6736-6745
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Sakaguchi Rayn^ORCID,Morton Kenneth D.,Collins Leslie M.,Torrione Peter A.

Funder

U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate, via a Grant Administered by the Army Research Office

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/8118236/08013058.pdf?arnumber=8013058

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Ground-Penetrating Radar (GPR)-Based Mapping of Underground Pipeline Network Distribution;IEEE Sensors Journal;2024-06-15

2. A Comprehensive Review of Conventional and Deep Learning Approaches for Ground-Penetrating Radar Detection of Raw Data;Applied Sciences;2023-07-07

3. Buried object detection from GPR images using improved U-net;International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022);2023-02-10

4. Sparsity enhanced MRF algorithm for automatic object detection in GPR imagery;Mathematical Biosciences and Engineering;2023

5. Investigating Transfer Learning Performances of Deep Learning Models for Classification of GPR B-Scan Images;Traitement du Signal;2022-11-30

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