Application of Neural Networks to Matlab Analyzed Hyperspectral Images for Characterization of Composite Structures

Mahmoud Z. Iskandarani

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

DOI: 10.4236/jilsa.2013.53016                

Abstract

A novel approach to damage detection in composite structures using hyperspectral image index analysis algorithm with neural network modeling employing Weight Elimination Algorithm (WEA) is presented and discussed. The matrix band based technique allows the monitoring and analysis of a component’s structure based on correlation between sequentially pulsed thermal images. The technique produces several matrices resulting from frame deviation and pixel redistribution calculations with ability for 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; PVT; Image Analysis; Damage Detection; Classification; Neural Networks; WEA

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	S. Holland and J. Renshawb, “Physics-Based Image En hancement for Infrared Thermography,” NDT & E Inter national, Vol. 43, No. 5, 2011, pp. 1-15.
[2]	C. Spiessberger, A. Dillenz and T. Zweschper, “Active Thermography for Quantitative NDT of CFRP Compo nents,” NDT in Aerospace, Vol. 3, No. 2, 2010, pp. 1-7.
[3]	M. Kutin, S. Ristic, M. Puharic, M. Vilotijevic and M. Krmar, “Thermographic Testing Of Epoxy-Glass Com posite Tensile Properties,” Contemporary Materials, Vol. II, No. 1, 2011, pp. 88-93. doi:10.5767/anurs.cmat.110201.en.088K
[4]	G. Vijayaraghavan, M. Majumder and K. Ramachandran, “Quantitative Analysis of Delaminations in GRP Pipes Using Thermal NDTE Technique,” Journal of Advanced Research in Mechanical Engineering Vol. 1, No. 1, 2010, pp. 60-68.
[5]	G. Wróbel, Z. Rdzawski, G. Muzia and S. Pawlak, “Quan titative Analysis of the Fibre Content Distribution in CFRP Composites Using Thermal Non-Destructive Test ing,” International Scientific Journal, Vol. 41, No. 1, 2010, pp. 28-36.
[6]	J. Casey and J. Kerekes, “Misregistration Impacts on Hy perspectral Target Detection,” Journal of Applied Re mote Sensing, Vol. 3, No. 1, 2009, Article ID: 033513.
[7]	A. Picon, O. Gita, P. Whelan and P. Iriondo, “Fuzzy Spectral and Spatial Feature Integration for Classification of Nonferrous Materials in Hyperspectral Data,” IEEE Transactions on Industrial Informatics, Vol. 5, No. 4, 2009, pp. 483-494. doi:10.1109/TII.2009.2031238
[8]	J. Fu, H. Caulfield, D. Wu and W. Tadesse, “Hyper spectral Image Analysis Using Artificial Color,” Journal of Applied Remote Sensing, Vol. 4, No. 1, 2010, Article ID: 043514.
[9]	J. Carven-Jones, M. Cudenov, M. Stapelbroek and E. Dereniak, “Infrared Hyperspectral Imaging Polarimeter Using Birefringent Prisms,” Applied Optics, Vol. 50, No. 8, 2011, pp. 1170-1185. doi:10.1364/AO.50.001170
[10]	A. Weber-Bargioni, A. Schwartzberg, M. Cornaglia, A. Ismatch, J. Urban, Y. Pang, R. Gordon, J. Bokor, M. Sal meron, D. Ogletree, P. Ashby, S. Cabrini and P. Schuck, “Hyperspectral Nanoscale Imaging on Dielectric Sub strates with Coaxial Optical Antenna Scan Probes,” Nano Letters, Vol. 11, No. 3, 2011, pp. 1201-1207. doi:10.1021/nl104163m
[11]	A. Abd-Elrahman, R. Pandi-Chhetri and G. Vallad, “De sign and Development of a Multi-Purpose Low-Cost Hy perspectral Imaging System,” Remote Sensing, Vol. 3, No. 3, 2011, pp. 570-586.
[12]	F. Kruse, J. Taranik, M. Coolbaugh, J. Michaels, E. Littlefield, W. Calvin and B. Martini, “Effect of Reduced Spatial Resolution on Mineral Mapping Using Imaging Spectrometry—Examples Using Hyperspectral Infrared Imager (HyspIRI)-Simulated Data,” Remote Sensing, Vol. 3, No. 8, 2011, pp. 1584-1602. doi:10.3390/rs3081584
[13]	D. Yudovsky, A. Nouvong, K. Schomacker and L. Pilon, “Assessing Diabetic Foot Ulcer Development Risk with Hyperspectral Tissue Oximetry,” Journal of biomedical Optics, Vol. 16, No. 2, 2011, Article ID: 026009. doi:10.1117/1.3535592
[14]	P. Vines, C. Tan, J. David, R. Attaluri, T. Vandervelde, S. Krishna, W. Jang and M. Hayat, “Versatile Spectral Imaging with an Algorithm-Based Spectrometer Using Highly Tuneable Quantum Dot Infrared Photodetectors,” IEEE Journal of Quantum Electronics, Vol. 47, No. 2, 2011, pp. 190-197. doi:10.1109/JQE.2010.2065216
[15]	A. Plaza, Q. Du, Y. Chang and R. King, “High Perform ance Computing for Hyperspectral Remote Sensing,” IEEE Journal Of Selected Topics in Applied Earth Ob servations and Remote Sensing, Vol. 4, No. 3, 2011, pp. 528-544. doi:10.1109/JSTARS.2010.2095495
[16]	M. Chikr El-Mezouar, N. Taleb, K. Kpalma and J. Ronsin, “An IHS Based Fusion for Color Distortion Reduction and Vegetation Enhancement in IKONOS Imagery,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 5, 2011, pp. 1590-1602. doi:10.1109/TGRS.2010.2087029
[17]	M. Chikr El-Mezouar, N. Taleb, K. Kpalma and J. Ronsin, “Vegetation Extraction from IKONOS Imagery Using High Spatial Resolution Index,” Journal of Applied Re mote Sensing, Vol. 5, No. 1, 2011, Article ID: 053543. doi:10.1117/1.3624518
[18]	X. Li, L. Li and M. He, “A Novel Pan Sharpening Algo rithm for WorldView-2 Satellite Images,” International Conference on Industrial and Intelligent Information (ICIII 2012), Vol. 31, Singapore, 17-18 March 2012, pp. 18-23.