Application of Hyperspectral Band Elimination Technique to PVT Images of Composite Structures

Mahmoud Zaki Iskandarani

Faculty of Science and Information Technology, Department of Computer Science, Al-Zaytoonah University of Jordan, Amman, Jordan.

DOI: 10.4236/eng.2012.410090                

Abstract

A new approach to NDT of composite structures using Band Elimination of the analyzed image index by Hyperspectral image analysis approach is presented and discussed. The matrix Band Elimination technique allows the monitoring and analysis of a components structure based on Filtering of bands and correlation between sequentially pulsed thermal images and their indices. The technique produces several matrices resulting from frame deviation and pixel redistribution calculations for intelligent classification and property prediction. The obtained results proved the technique to be capable of identifying damaged components with ability to model various types of damage under different conditions.

Keywords

Hyperspectral; Thermography; NDE; Composites; Damage Detection; PVT; Neural Networks

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	D. P. Myriounis, E. Z. Kordatos, S. T. Hasan and T. E. Matikas, “Crack-Tip Stress Field and Fatigue Crack Growth Monitoring Using Infrared Lock-In Thermography in A359/SiCp Composites,” Strain, Vol. 47, No. s1, 2011, pp. e619-e627. doi:10.1111/j.1475-1305.2009.00665.x
[2]	B. B. Lahiria, S. Bagavathiappana, P. R. Reshmib, John Philipa, T. Jayakumara and B. Raja, “Quantification of Defects in Composites and Rubber Materials Using Active Thermography,” Infrared Physics & Technology, Vol. 55, No. 2-3, 2012, pp. 191-199. doi:10.1016/j.infrared.2012.01.001
[3]	M. Naderi, A. Kahirdeh and M. M. Khonsari, “Dissipated Thermal Energy and Damage Evolution of Glass/Epoxy Using Infrared Thermography and Acoustic Emission,” Composites Part B: Engineering, Vol. 43, No. 3, 2012, pp. 1613-1620.
[4]	C. Q. Wu, W. P. Wang, Q. G. Yuan, Y. J. Li, W. Zhang and X. D. Zhang, “Infrared Thermography Non-Destructive Testing of Composite Materials,” Advanced Materials Research, Vol. 291-294, 2011, pp. 1307-1310. doi:10.4028/www.scientific.net/AMR.291-294.1307
[5]	L. Cheng and G. Y. Tian, “Comparison of Nondestructive Testing Methods on Detection of Delaminations in Composites,” Journal of Sensors, Vol. 2012, No. 2012, 2012, Article ID: 408437.
[6]	M. Kutin, S. Risti?, M. Puhari?, M. Vilotijevi? and M. Krmar, “Thermographic Testing of Epoxy-Glass Composite Tensile Properties,” Contemporary Materials, Vol. II, No. 2, 2011, pp. 88-93.
[7]	P. Baranowski, W. Mazurek, J. Wozniak and U. Majewska, “Detection of Early Bruises in Apples Using Hyperspectral Data and Thermal Imaging,” Journal of Food Engineering, Vol. 110, No. 3, 2012, pp. 345-355. doi:10.1016/j.jfoodeng.2011.12.038
[8]	F. D. van der Meer, H. A. van der Werff, F. A. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. M. Carranza, J. Boudewijn de Smeth and T. Woldai, “Multiand Hyperspectral Geologic Remote Sensing: A Review,” International Journal of Applied Earth Observation and Geoinformation, Vol. 14, No. 1, 2012, pp. 112-128. doi:10.1016/j.jag.2011.08.002
[9]	F. Liu, F. Seinstra and A. Plazac, “Parallel Hyperspectral Image Processing on Distributed Multicluster Systems,” Journal of Applied Remote Sensing, Vol. 5, No. 1, 2011, pp. 1-14.
[10]	A. Picón, O. Ghita, A. Bereciartua, J. Echazarra, P. Whelan and P. Iriondo, “Real-Time Hyperspectral Processing for Automatic Nonferrous Material Sorting,” Journal of Electronic Imaging, Vol. 21, No. 1, 2012, pp. 1-9.
[11]	Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng and Q. Pan, “Hyperspectral Imagery Super-Resolution by Sparse Representation and Spectral Regularization,” EURASIP Journal on Advances in Signal Processing, Vol. 2011, 2011, p. 87.
[12]	R. Darvishzadeha, C. Atzbergerb, A. Skidmorec and M. Schlerfc, “Mapping Grassland Leaf Area Index with Airborne Hyperspectral Imagery: A Comparison Study of Statistical Approaches and Inversion of Radiative Transfer Models,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 6, 2011, pp. 894-906. doi:10.1016/j.isprsjprs.2011.09.013
[13]	I. Amro, J. Mateos, M. Vega, R. Molina and A. Katsaggelos, “A Survey of Classical Methods and New Trends in Pansharpening of Hyperspectral Images,” EURASIP Journal on Advances in Signal Processing, Vol. 2011, 2011, p. 79.
[14]	B. Aiazzi, L. Alparone, S. Baronti, C. Lastri and M. Selva1, “Spectral Distortion in Lossy Compression of Hyperspectral Data,” Journal of Electrical and Computer Engineering, Vol. 2012, No. 2012, 2012, Article ID: 850637.
[15]	A. Mahyari and M. Yazdi, “Panchromatic and Hyperspectral Image Fusion Based on Maximization of Both Spectral and Spatial Similarities,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 6, 2011, pp. 1976-1985. doi:10.1109/TGRS.2010.2103944
[16]	G. Camps-Valls, J. Benediktsson, L. Bruzzone and J. Chanussot, “Introduction to the Issue on Advances in Remote Sensing Image Processing,” IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No. 3, 2011, pp. 365-369. doi:10.1109/JSTSP.2011.2142490