Anomaly detection using classified eigenblocks in GPR image

Min Ju Kim; Seong Dae Kim; Seung-eui Lee

doi:10.1117/12.2227148

3 May 2016 Anomaly detection using classified eigenblocks in GPR image

Min Ju Kim, Seong Dae Kim, Seung-eui Lee

Proceedings Volume 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI; 98231F (2016) https://doi.org/10.1117/12.2227148
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Min Ju Kim, Seong Dae Kim, and 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); https://doi.org/10.1117/12.2227148

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available