NDLI: Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data

Content Provider	IEEE Xplore Digital Library
Author	Torrione, P.A. Morton, K.D. Sakaguchi, R. Collins, L.M.
Copyright Year	1980
Abstract	Ground-penetrating radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. However, sophisticated processing of GPR data is necessary to reduce false alarms due to naturally occurring subsurface clutter and soil distortions. Most currently fielded GPR-based landmine detection algorithms utilize feature extraction and statistical learning to develop robust classifiers capable of discriminating buried threats from inert subsurface structures. Analysis of these techniques indicates strong underlying similarities between efficient landmine detection algorithms and modern techniques for feature extraction in the computer vision literature. This paper explores the relationship between and application of one modern computer vision feature extraction technique, namely histogram of oriented gradients (HOG), to landmine detection in GPR data. The results presented indicate that HOG features provide a robust tool for target identification for both classification and prescreening and suggest that other techniques from computer vision might also be successfully applied to target detection in GPR data.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	1539
Ending Page	1550
Page Count	12
File Size	1131648
File Format	PDF
ISSN	01962892
Volume Number	52
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	random forest Computer vision edge histogram descriptors ground-penetrating radar (GPR) histogram of oriented gradients (HOG)
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Histogram of gradient features for buried threat detection in ground penetrating radar data

Automatic target detection in GPR images using Histogram of Oriented Gradients (HOG)

A Novel Clutter Suppression Algorithm for Landmine Detection With GPR

A Large-Scale Systematic Evaluation of Algorithms Using Ground-Penetrating Radar for Landmine Detection and Discrimination

Application of feature extraction methods for landmine detection using the Wichmann/Niitek ground-penetrating radar

Filtering Soil Surface and Antenna Effects From GPR Data to Enhance Landmine Detection

Multiple-Instance Hidden Markov Model for GPR-Based Landmine Detection

Exploiting Ground-Penetrating Radar Phenomenology in a Context-Dependent Framework for Landmine Detection and Discrimination

Influence of Heterogeneous Soils and Clutter on the Performance of Ground-Penetrating Radar for Landmine Detection

Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data

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Application of feature extraction methods for landmine detection using the Wichmann/Niitek ground-penetrating radar

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Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data