Application of independent component analysis for evaluation of ashlar masonry walls

[EN] This paper presents a novel application of Independent Component Analysis (ICA) to the evaluation of ashlar masonry walls inspected with Ground Penetrating Radar (GPR). ICA is used as preprocessor to eliminate the background from the backscattered signals. Thus, signal-to-noise ratio of the GPR signals is enhanced. Several experiments were made on scale models of historic ashlar masonry walls. These models were loaded with different weights, and the corresponding B-Scans were obtained. ICA shows the best performance to enhance the quality of the B-Scans compared with classical methods used in GPR signal processing.This work has been supported by the Generalitat Valenciana under grant PROMETEO/2010/040, and the Spanish Administration and the FEDER Programme of the European Union under grant TEC 2008-02975/TEC.Salazar Afanador, A.; Safont Armero, G.; Vergara Domínguez, L. (2011). Application of independent component analysis for evaluation of ashlar masonry walls. Lecture Notes in Computer Science. 6691(1):469-476. https://doi.org/10.1007/978-3-642-21498-1_5946947666911Salazar, A., Unió, J.M., Serrano, A., Gosalbez, J.: Neural networks for defect detection in non-destructive evaluation by sonic signals. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 638–645. Springer, Heidelberg (2007)Salazar, A., Vergara, L., Llinares, R.: Learning material defect patterns by separating mixtures of independent component analyzers from NDT sonic signals. Mechanical Systems and Signal processing 24(6), 1870–1886 (2010)Zhao, A., Jiang, Y., Wang, W.: Exploring Independent Component Analysis for GPR Signal Processing. In: Progress In Electromagnetics Research Symposium 2005, pp. 750–753. The Electromagnetics Academy, Cambridge (2005)Abujarad, F., Omar, A.: Comparison of Independent-Component Analysis (ICA) Algorithms for GPR Detection of Non-Metallic Land Mines. In: Bruzzone, L. (ed.) Proceedings of SPIE Image and Signal Processing for Remote Sensing XII, vol. 6365, pp. 636516.1–636516.12. SPIE, Bellingham (2006)Liu, J.X., Zhang, B., Wu, R.B.: GPR Ground Bounce Removal Methods Based on Blind Source Separation. In: Progress In Electromagnetics Research Symposium 2006, pp. 256–259. The Electromagnetics Academy, Cambridge (2006)Verma, P.K., Gaikwad, A.N., Sigh, D., Nigam, M.J.: Analysis of Clutter Reduction Techniques for Through Wall Imaging in UWB Range. In: Progres. Electromagnetics Research B 2009, vol. 17, pp. 29–48. The Electromagnetics Academy, Cambridge (2009)Salazar, A., Vergara, L., Serrano, A., Igual, J.: A General Procedure for Learning Mixtures of Independent Component Analyzers. Pattern Recognition 43(1), 69–85 (2010)Cardoso, J.F., Souloumiac, A.: Blind beamforming for non Gaussian signals. IEE Proceedings-F 140(6), 362–370 (1993)Ziehe, A., Muller, K.R.: TDSEP - An Efficient Algorithm for Blind Separation Using Time Structure. In: Proceedings of the Eighth International Conference on Artificial Neural Networks ICANN 1998, Perspectives in Neural Computing, pp. 675–680 (1998)Reynolds, J.M.: An Introduction to Applied and Environmental Geophysics. Wiley, Chichester (1997)Igual, J., Camacho, A., Vergara, L.: A blind source separation technique for extracting sinusoidal interferences in ultrasonic non-destructive testing. Journal of VLSI Signal Processing 38, 25–34 (2004)Salazar, A., Gosálbez, J., Igual, J., Llinares, R., Vergara, L.: Two applications of independent component analysis for non-destructive evaluation by ultrasounds. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 406–413. Springer, Heidelberg (2006)Raghavan, R.S.: A Model for Spatially Correlated Radar Clutter. IEEE Trans. on Aerospace and Electronic Systems 27, 268–275 (1991)Salazar, A., Vergara, L.: ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics. EURASIP Journal on Advances in Signal Processing, Article ID 125201, 11 (2010), doi:10.1155/2010/125201Salazar, A., Vergara, L., Miralles, R.: On including sequential dependence in ICA mixture models. Signal Processing 90(7), 2314–2318 (2010