Mineral identification in LWIR hyperspectral imagery applying sparse-based clustering

References

• Creagh DC, Bradley D. Physical techniques in the study of art, archaeology and cultural heritage. Vol. 2. Amsterdam, The Netherlands: Elsevier; 2007.
   Google Scholar
• Carlomagno GM, Di Maio R, Meola C, et al. Infrared thermography and geophysical techniques in cultural heritage conservation. Quant Infrared Thermogr J. 2005;2:5–24.
   Google Scholar
• Zhang H, Sfarra S, Saluja K, et al. Non-destructive investigation of paintings on canvas by continuous wave terahertz imaging and flash thermography. J Nondestr Eval. 2017;36(2):34.
   Web of Science ®Google Scholar
• Haboudane D, Miller JR, Pattey E, et al. Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ. 2004;90:337–352.
   Web of Science ®Google Scholar
• Camoglu G, Demirel K, Genc L. Use of infrared thermography and hyperspectral data to detect effects of water stress on pepper. Quant Infrared Thermogr J. 2018;15(1):81–94.
   Web of Science ®Google Scholar
• Zhang H, Sfarra S, Sarasini F, et al. Optical and mechanical excitation thermography for impact response in basalt-carbon hybrid fiber-reinforced composite laminates. IEEE Trans Ind Inform. 2017;14:514–522.
   Web of Science ®Google Scholar
• Zhang H, Robitaille F, Grosse CU, et al. Optical excitation thermography for twill/plain weaves and stitched fabric dry carbon fibre preform inspection. Compos Part A Appl Sci Manuf. 2018;107:282–293.
   Web of Science ®Google Scholar
• Fernandes H, Zhang H, Ibarra-Castanedo C, et al. Fiber orientation assessment on randomly-oriented strand composites by means of infrared thermography. Compos Sci Technol. 2015;121:25–33.
   Web of Science ®Google Scholar
• Wu J-Y, Sfarra S, Yao Y. Sparse principal component thermography for subsurface defect detection in composite products. IEEE Trans Ind Inform. 2018;17: 1–1.
   Google Scholar
• Lewis EN, Freeman JE. Multispectral/hyperspectral medical instrument. US Patent 6,937,885. 2005 Aug 30.
   Google Scholar
• Akbari H, Uto K, Kosugi Y, et al. Cancer detection using infrared hyperspectral imaging. Cancer Sci. 2011;102:852–857.
   PubMed Web of Science ®Google Scholar
• Akbari H, Kosugi Y. Hyperspectral imaging: A new modality in surgery. In: Naik GR, editor. Recent advances in biomedical engineering. InTech; 2009.
   Google Scholar
• Lalanne N, Krapez J-C, Le Niliot C, et al. Development and validation of a numerical tool for simulating the surface temperature field and the infrared radiance rendering in an urban scene. Quant Infrared Thermogr J. 2015;12:196–218.
   Web of Science ®Google Scholar
• Bishop CA, Liu JG, Mason PJ. Hyperspectral remote sensing for mineral exploration in pulang, yunnan province, china. Int J Remote Sens. 2011;32:2409–2426.
   Web of Science ®Google Scholar
• Ahi K, Shahbazmohamadi S, Asadizanjani N. Quality control and authentication of packaged integrated circuits using enhanced-spatial-resolution terahertz time-domain spectroscopy and imaging. Opt Lasers Eng. 2018;104:274–284.
   Web of Science ®Google Scholar
• Ahi K. A method and system for enhancing the resolution of terahertz imaging. Measurement. 2018;17:1–8.
   Google Scholar
• Olbrycht R, Kałuża M, Wittchen W, et al. Gas identification and estimation of its concentration in a tube using thermographic camera with diffraction grating. Quant Infrared Thermogr J. 2017;15:1–15.
   Web of Science ®Google Scholar
• Kruse FA, Boardman JW, Huntington JF. Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping. IEEE Trans Geosci Remote Sens. 2003;41:1388–1400.
   Web of Science ®Google Scholar
• Chen X, Warner TA, Campagna DJ. Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at cuprite, nevada. Remote Sens Environ. 2007;110:344–356.
   Web of Science ®Google Scholar
• Yousefi B, Sojasi S, Castanedo CI, et al. Mineral identification in hyperspectral imaging using sparse-pca. In: Zalameda JN, Bison P, editors. Thermosense: thermal infrared applications XXXVIII. Vol. 9861. Baltimore, Maryland: International Society for Optics and Photonics; 2016. p. 986118.
   Google Scholar
• MartÍnez-UsÓMartinez-Uso A, Pla F, Sotoca JM, et al. Clustering-based hyperspectral band selection using information measures. IEEE Trans Geosci Remote Sens. 2007;45:4158–4171.
   Web of Science ®Google Scholar
• Plaza A, Valencia D, Plaza J, et al. Commodity cluster-based parallel processing of hyperspectral imagery. J Parallel Distrib Comput. 2006;66:345–358.
   Web of Science ®Google Scholar
• Wu J, Kong W, Mielikainen J, et al. Lossless compression of hyperspectral imagery via clustered differential pulse code modulation with removal of local spectral outliers. IEEE Signal Process Lett. 2015;22:2194–2198.
   Web of Science ®Google Scholar
• Liu Y, Li J, Plaza A. Spectrometer-driven spectral partitioning for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens. 2016;9:668–680.
   Web of Science ®Google Scholar
• Zhou J, Kwan C, Ayhan B, et al. A novel cluster kernel rx algorithm for anomaly and change detection using hyperspectral images. IEEE Trans Geosci Remote Sens. 2016;54:6497–6504.
   Web of Science ®Google Scholar
• Hartigan JA, Wong MA. Algorithm as 136: A k-means clustering algorithm. J R Stat Soc Ser C Appl Stat. 1979;28:100–108.
   Google Scholar
• Jain AK. Data clustering: 50 years beyond k-means. Pattern Recognit Lett. 2010;31:651–666.
   Web of Science ®Google Scholar
• Dhillon IS, Guan Y, Kulis B. Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. New York, NY, USA: ACM; 2004. p. 551–556.
   Google Scholar
• Nasrabadi NM. Pattern recognition and machine learning. J Electron Imaging. 2007;16:049901.
   Google Scholar
• Jolliffe IT. Principal component analysis and factor analysis. In: Principal component analysis. New York, NY: Springer; 1986. p. 115–128.
   Google Scholar
• Ng AY, Jordan MI, Weiss Y. On spectral clustering: analysis and an algorithm. In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing systems. Vancouver, British Columbia, Canada: 2002. p. 849–856.
   Google Scholar
• Mika S, Schölkopf B, Smola AJ, et al. Kernel pca and de-noising in feature spaces. In: Solla SA, Leen TK, Müller K, editors. Advances in neural information processing systems. Denver, Colorado: 1999. p. 536–542.
   Google Scholar
• Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis. Cambridge, UK: Cambridge university press; 2004.
   Google Scholar
• Sjöstrand K, Clemmensen LH, Larsen R, et al. Spasm: A matlab toolbox for sparse statistical modeling. J Stat Softw. 2018;84(10).
   Web of Science ®Google Scholar
• Schölkopf B, Smola A, Müller K-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998;10:1299–1319.
   Web of Science ®Google Scholar
• Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol. 1996;58:267–288.
   Web of Science ®Google Scholar
• Tibshirani R. The lasso method for variable selection in the cox model. Stat Med. 1997;16:385–395.
   PubMed Web of Science ®Google Scholar
• Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 2005;67:301–320.
   Web of Science ®Google Scholar
• Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. J Comput Graph Stat. 2006;15:265–286.
   Web of Science ®Google Scholar
• Tillmann AM, Pfetsch ME. The computational complexity of the restricted isometry property, the nullspace property, and related concepts in compressed sensing. IEEE Trans Inf Theory. 2014;60:1248–1259.
   Google Scholar
• Ding C, He X. K-means clustering via principal component analysis. Proceedings of the twenty-first international conference on Machine learning; 2004. Banff, Alberta, Canada: ACM; p. 29.
   Google Scholar
• Marukatat S. Sparse kernel pca by kernel k-means and preimage reconstruction algorithms. Pacific Rim International Conference on Artificial Intelligence; 2006. Guilin, China: Springer; pp. 454–463.
   Google Scholar
• Zha H, He X, Ding C, et al. Spectral relaxation for k-means clustering. In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing systems. Vancouver, British Columbia, Canada: 2002. p. 1057–1064.
   Google Scholar
• Cohen MB, Elder S, Musco C, et al. Dimensionality reduction for k-means clustering and low rank approximation. In:Rubinfeld R, editor. Proceedings of the forty-seventh annual ACM symposium on theory of computing. Portland, Oregon, USA: ACM; 2015. p. 163–172.
   Google Scholar
• Yousefi B, Sojasi S, Castanedo CI, et al. Emissivity retrieval from indoor hyperspectral imaging of mineral grains. In: Zalameda JN, Bison P, editors. Thermosense: thermal infrared applications XXXVIII. Vol. 9861. Baltimore, United States: International Society for Optics and Photonics; 2016. p. 98611C.
   Google Scholar
• Yousefi B, Sojasi S, Castanedo CI, et al. Continuum removal for ground based lwir hyperspectral infrared imagery applying non-negative matrix factorization. Appl Opt. 2018;57:6219–6228.
   PubMed Web of Science ®Google Scholar
• Baldridge A, Hook S, Grove C, et al. The aster spectral library version 2.0. Remote Sens Environ. 2009;113:711–715.
   Web of Science ®Google Scholar
• Yousefi B, Sojasi S, Liaigre K, et al. Modified algorithm for mineral identification in lwir hyperspectral imagery. In: Bison P, Burleigh D, editors. Thermosense: thermal infrared applications XXXIX. Vol. 10214. International Society for Optics and Photonics; 2017. p. 102141H.
   Google Scholar
• Kruse F. Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra. Int J Remote Sens. 1996;17(9):1623–1632.
   Web of Science ®Google Scholar
• Balick L, Gillespie A, French A, et al. Longwave thermal infrared spectral variability in individual rocks. IEEE Geosci Remote Sens Lett. 2009;6(1):52–56.
   Web of Science ®Google Scholar
• Yousefi B, Sojasi S, Castanedo CI, et al. Comparison assessment of low rank sparse-pca based-clustering/classification for automatic mineral identification in long wave infrared hyperspectral imagery. Infrared Phys Technol. 2018;93:103–111.
   Web of Science ®Google Scholar
• Maldague XP. Nondestructive evaluation of materials by infrared thermography. Berlin: Springer Science & Business Media; 2012.
   Google Scholar
• Fernandes H, Zhang H, Figueiredo A, et al. Machine learning and infrared thermography for fiber orientation assessment on randomly-oriented strands parts. Sensors. 2018;18(1):288.
   Web of Science ®Google Scholar