Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species

Sensors (Switzerland)

Volumn 12, Issue 7, 2012, Pages 8755-8769

  Ullah, Saleem ^a   Groen, Thomas A ^a   Schlerf, Martin ^b   Skidmore, Andrew K ^a   Nieuwenhuis, Willem ^a   Vaiphasa, Chaichoke ^c  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b LUXEMBOURG INSTITUTE OF SCIENCE AND TECHNOLOGY   (Luxembourg)

^c CHULALONGKORN UNIVERSITY   (Thailand)

Author keywords

Genetic algorithms; Spectral emissivity; Spectral separability; Thermal infrared remote sensing

Indexed keywords

EID: 84864349714     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s120708755     Document Type: Article

References (46)

1. Adam, E.; Mutanga, O. Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS J. Photogram. Remote Sens. 2009, 64, 612-620.
2. Cho, M.A.; Debba, P.; Mathieu, R.; Naidoo, L.; van Aardt, J.; Asner, G.P. Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: Canopy-level analysis. IEEE Trans. Geosci. Remote Sens. 2010, 48, 4133-4142.
3. Schmidt, K.S.; Skidmore, A.K. Spectral discrimination of vegetation types in a coastal wetland. Remote Sens. Environ. 2003, 85, 92-108.
4. Ustin, S.L.; Xiao, Q.F. Mapping successional boreal forests in interior central Alaska. Int. J. Remote Sens. 2001, 22, 1779-1797.
5. Landgrebe, D.A. Signal Theory Methods in Multispectral Remote Sensing; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2003; p. 528.
6. Hao, X.; Qu, J.J. Fast and highly accurate calculation of band averaged radiance. Int. J. Remote Sens. 2009, 30, 1099-1108.
7. Hughes, G.F. On mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 1968, 14, 55.
8. Vaiphasa, C.; Skidmore, A.K.; de Boer, W.F.; Vaiphasa, T. A hyperspectral band selector for plant species discrimination. ISPRS J. Photogram. Remote Sens. 2007, 62, 225-235.
9. Zhou, M.D.; Shu, J.O.; Chen, Z.G. Classification of hyperspectral remote sensing image based on genetic algorithm and SVM. In Remote Sensing and Modeling of Ecosystems for Sustainability VII; Gao, W., Jackson, T.J., Wang, J., Eds.; Spie-Int Soc Optical Engineering: Bellingham, WA, USA, 2010; Volume 7809.
10. Shahshahani, B.M.; Landgrebe, D.A. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Trans. Geosci. Remote Sens. 1994, 32, 1087-1095.
11. Rui, H.; Mingyi, H. Band selection based on feature weighting for classification of hyperspectral data. IEEE Geosci. Remote Sens. Lett. 2005, 2, 156-159.
12. Du, Q. Independent component analysis to hyperspectral image classification. In Imaging Spectrometry X; Shen, S.S., Lewis, P.E., Eds.; Spie-Int Soc Optical Engineering: Bellingham, WA, USA, 2004; Volume 5546, pp. 366-373.
13. Ifarraguerri, A.; Chang, C.I. Unsupervised hyperspectral image analysis with projection pursuit. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2529-2538.
14. Kaewpijit, S.; Le moigne, J.; El-Ghazawi, T. Automatic reduction of hyperspectral imagery using wavelet spectral analysis. IEEE Trans. Geosci. Remote Sens. 2003, 41, 863-871.
15. Lee, C.H.; Landgrebe, D.A. Feature-extraction based on decision boundaries. IEEE Trans. Patt. Anal. Mach. Intell. 1993, 15, 388-400.
16. Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning; Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, USA, 1989; p. 372.
17. Holland, J. Adaptation in Natural and Artificial Systems; University of Michigan: Ann Arbor, MI, USA, 1975.
18. Fang, H.; Liang, S.; Kuusk, A. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sens. Environ. 2003, 85, 257-270.
19. Kawamura, K.; Watanabe, N.; Sakanoue, S.; Lee, H.-J.; Inoue, Y.; Odagawa, S. Testing genetic algorithm as a tool to select relevant wavebands from field hyperspectral data for estimating pasture mass and quality in a mixed sown pasture using partial least squares regression. Grassland Sci. 2010, 56, 205-216.
20. Keshava, N. Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1552-1565.
21. Leardi, R. Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection. J. Chemometr. 1994, 8, 65-79.
22. Leardi, R.; Lupiáñez González, A. Genetic algorithms applied to feature selection in PLS regression: How and when to use them. Chemometr. Intell. Lab. Syst. 1998, 41, 195-207.
23. Siedlecki, W.; Sklansky, J. A note on genetic algorithms for large-scale feature selection. Patt. Recogn. Lett. 1989, 10, 335-347.
24. Yu, S.; Backer, S.D.; Scheunders, P. Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery. Patt. Recogn. Lett. 2002, 23, 183-190.
25. Anderson, J.R.; Hardy, E.E.; Roach, J.T.; Witmer, R.E. A Land Use and Land Cover Classification System for Use with Remote Sensor Data; Geological Survey, United States Government Printing Office: Washington, DC, USA, 1976.
26. Ribeiro da Luz, B.; Crowley, J.K. Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm). Remote Sens. Environ. 2007, 109, 393-405.
27. Ribeiro da Luz, B. Attenuated total reflectance spectroscopy of plant leaves: A tool for ecological and botanical studies. New Phytol. 2006, 172, 305-318.
28. Ribeiro da Luz, B.; Crowley, J.K. Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0-13.5 μm) imagery. Remote Sens. Environ. 2010, 114, 404-413.
29. Ullah, S.; Schlerf, M.; Skidmore, A.K.; Hecker, C. Identifying plant species using mid-wave infrared (2.5-6.0 μm) and thermal infrared (8-140 μm) emissivity spectra. Remote Sens. Environ. 2012, 118, 95-102.
30. Nicodemus, F.E. Directional reflectance and emissivity of an opaque surface. Appl. Opt. 1965, 4, 767-773.
31. Salisbury, J.W.; Wald, A.; D'Aria, D.M. Thermal-infrared remote sensing and Kirchhoff's law 1. Laboratory measurements. J. Geophys. Res. 1994, 99, 11897-11911.
32. Salisbury, J.W.; Milton, N.M. Thermal infrared (2.5-13.5 μm) directional hemispherical reflectance of leaves. Photogramm. Eng. Remote Sens. 1988, 54, 1301-1304.
33. Hecker, C.; Hook, S.; Meijde, M.; Bakker, W.; Werff, H.; Wilbrink, H.; Ruitenbeek, F.; Smeth, B.; Meer, F. Thermal infrared spectrometer for earth science remote sensing applications-Instrument modifications and measurement procedures. Sensors 2011, 11, 10981-10999.
34. Richards, J.A.; Jia, X. Remote Sensing Digital Image Analysis: An Introduction, 4th ed.; Springer-Verlag: Berlin, Heidelberg, Germany, 2006.
35. Achenbach, H.; Lottes, M.; Waibel, R.; Karikas, G.A.; Correa, M.D.; Gupta, M.P. Constituents of tropical medicinal-plants; Alkaloids and other compounds from psychotria-correae. Phytochemistry 1995, 38, 1537-1545.
36. Elvidge, C.D. Thermal infrared reflectance of dry plant materials: 2.5-20.0 μm. Remote Sens. Environ. 1988, 26, 265-285.
37. Heredia, A. Biophysical and biochemical characteristics of cutin, a plant barrier biopolymer. BBA Gener. Subj. 2003, 1620, 1-7.
38. Holloway, P.J. Structure and Histochemistry of Plant Cuticular Membrane: An Overview; Acadmic Press: London, UK, 1982.
39. Fabre, S.; Lesaignoux, A.; Olioso, A.; Briottet, X. Influence of water content on spectral reflectance of leaves in the 3-15 μm domain. IEEE Geosci. Remote Sens. Lett. 2011, 8, 143-147.
40. Gerber, F.; Marion, R.; Olioso, A.; Jacquemoud, S.; da Luz, B.R.; Fabre, S. Modeling directional-hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 μm to 5.7 μm with the PROSPECT-VISIR model. Remote Sens. Environ. 2011, 115, 404-414.
41. Maréchal, Y.; Chanzy, H. The hydrogen bond network in Iβ cellulose as observed by infrared spectrometry. J. Mol. Struct. 2000, 523, 183-196.
42. Kacuráková, M.; Wilson, R.H. Developments in mid-infrared FT-IR spectroscopy of selected carbohydrates. Carbohydr. Polym. 2001, 44, 291-303.
43. Silverstein, R.M.; Webster, F.X. Spectrometric Identification of Organic Compounds, 6th ed.; John Wiley & Sons: New York, NY, USA, 1998; pp. 71-143.
44. Ramirez, F.J.; Luque, P.; Heredia, A.; Bukovac, M.J. Fourier-transform IR study of enzymatically isolated tomato fruit cuticular membrane. Biopolymers 1992, 32, 1425-1429.
45. Fry, S.C. Primary cell wall metabolism: Tracking the careers of wall polymers in living plant cells. New Phytol. 2004, 161, 641-675.
46. Wilson, R.H.; Smith, A.C.; Kacurakova, M.; Saunders, P.K.; Wellner, N.; Waldron, K.W. The mechanical properties and molecular dynamics of plant cell wall polysaccharides studied by Fourier-transform infrared spectroscopy. Plant Physiol. 2000, 124, 397-405.