Weighted principal component analysis for real-time background removal in GPR data

Yakov P. Shkolnikov

doi:10.1117/12.921116

10 May 2012 Weighted principal component analysis for real-time background removal in GPR data

Yakov P. Shkolnikov

Proceedings Volume 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII; 835720 (2012) https://doi.org/10.1117/12.921116
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Unprocessed ground penetrating radar (GPR) imagery often suffers from horizontal background striations owing to internal system noise and/or ground layers. These striations adversely affect the ability to identify buried objects, either via visual inspection of the imagery or by automatic target detection techniques. Singular value decomposition (SVD) is one of the most common techniques for removing these background striations, but it is hindered in real-time implementations due to its computational overhead. This paper proposes and demonstrates an alternative technique. The resulting background removal process based on weighted principal component analysis runs faster, preserves more of the target information, and removes a greater percentage of the background compared to standard SVD-based techniques.

Citation Download Citation

Yakov P. Shkolnikov "Weighted principal component analysis for real-time background removal in GPR data", Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 835720 (10 May 2012); https://doi.org/10.1117/12.921116

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