期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 52, 期 3, 页码 1539-1550出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2013.2252016
关键词
Computer vision; edge histogram descriptors; ground-penetrating radar (GPR); histogram of oriented gradients (HOG); random forest
类别
资金
- U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate [W911NF-09-1-0487, W911NF-06-1-0357]
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.
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