3 May 2016 Anomaly detection using classified eigenblocks in GPR image
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Abstract
Automatic landmine detection system using ground penetrating radar has been widely researched. For the automatic mine detection system, system speed is an important factor. Many techniques for mine detection have been developed based on statistical background. Among them, a detection technique employing the Principal Component Analysis(PCA) has been used for clutter reduction and anomaly detection. However, the PCA technique can retard the entire process, because of large basis dimension and a numerous number of inner product operations. In order to overcome this problem, we propose a fast anomaly detection system using 2D DCT and PCA. Our experiments use a set of data obtained from a test site where the anti-tank and anti- personnel mines are buried. We evaluate the proposed system in terms of the ROC curve. The result shows that the proposed system performs much better than the conventional PCA systems from the viewpoint of speed and false alarm rate.
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Min Ju Kim, Min Ju Kim, Seong Dae Kim, Seong Dae Kim, Seung-eui Lee, Seung-eui Lee, } "Anomaly detection using classified eigenblocks in GPR image", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231F (3 May 2016); doi: 10.1117/12.2227148; https://doi.org/10.1117/12.2227148
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