Journal of Spectroscopy

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1. D. G. Goodenough and T. Han, “Reducing noise in hyperspectal data — a nonlinear data series analysis approach,” in 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp. 1–4, Grenoble, France, August 2009. View at Publisher · View at Google Scholar · View at Scopus
2. A. Karami, R. Heylen, and P. Scheunders, “Band-specific shearlet-based hyperspectral image noise reduction,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 9, pp. 5054–5066, 2015. View at Publisher · View at Google Scholar · View at Scopus
3. X. Zhu and X. Wu, “Class noise vs. attribute noise: a quantitative study,” Artificial Intelligence Review, vol. 22, no. 3, pp. 177–210, 2004. View at Publisher · View at Google Scholar
4. J. A. Sáez, J. Luengo, and F. Herrera, “Evaluating the classifier behavior with noisy data considering performance and robustness: the equalized loss of accuracy measure,” Neurocomputing, vol. 176, pp. 26–35, 2016. View at Publisher · View at Google Scholar · View at Scopus
5. D. F. Nettleton, A. Orriols-Puig, and A. Fornells, “A study of the effect of different types of noise on the precision of supervised learning techniques,” Artificial Intelligence Review, vol. 33, no. 4, pp. 275–306, 2010. View at Publisher · View at Google Scholar · View at Scopus
6. J. S. Sánchez, R. Barandela, A. I. Marques, R. Alejo, and J. Badenas, “Analysis of new techniques to obtain quality training sets,” Pattern Recognition Letters, vol. 24, no. 7, pp. 1015–1022, 2003. View at Publisher · View at Google Scholar · View at Scopus
7. J. C. Harsanyi and C.-I. Chang, “Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach,” IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 4, pp. 779–785, 1994. View at Publisher · View at Google Scholar · View at Scopus
8. Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral image denoising employing a spectral–spatial adaptive total variation model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3660–3677, 2012. View at Publisher · View at Google Scholar · View at Scopus
9. D. Cerra, R. Muller, and P. Reinartz, “Noise reduction in hyperspectral images through spectral unmixing,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 1, pp. 109–113, 2014. View at Publisher · View at Google Scholar · View at Scopus
10. H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 8, pp. 4729–4743, 2014. View at Publisher · View at Google Scholar · View at Scopus
11. Y.-Q. Zhao and J. Yang, “Hyperspectral image denoising via sparse representation and low-rank constraint,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 296–308, 2015. View at Publisher · View at Google Scholar · View at Scopus
12. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 1, pp. 65–74, 1988. View at Publisher · View at Google Scholar · View at Scopus
13. T. M. Khoshgoftaar and P. Rebours, “Improving software quality prediction by noise filtering techniques,” Journal of Computer Science and Technology, vol. 22, no. 3, pp. 387–396, 2007. View at Publisher · View at Google Scholar · View at Scopus
14. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
15. Z. Bassa, U. Bob, Z. Szantoi, and R. Ismail, “Land cover and land use mapping of the iSimangaliso wetland park, South Africa: comparison of oblique and orthogonal random forest algorithms,” Journal of Applied Remote Sensing, vol. 10, no. 1, article 015017, 2016. View at Publisher · View at Google Scholar · View at Scopus
16. W. Chen, X. Li, and Y. Wang, “Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the Three Gorges, China,” Remote Sensing of Environment, vol. 152, pp. 291–301, 2014. View at Publisher · View at Google Scholar · View at Scopus
17. M. Belgiu and L. Drăguţ, “Random forest in remote sensing: a review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24–31, 2016. View at Publisher · View at Google Scholar · View at Scopus
18. B. H. Menze, B. M. Kelm, D. N. Splitthoff, U. Koethe, and F. A. Hamprecht, “On oblique random forests,” Machine Learning and Knowledge Discovery in Databases, vol. 6912, pp. 453–469, 2011. View at Publisher · View at Google Scholar · View at Scopus
19. T. N. Do, P. Lenca, S. Lallich, and N. K. Pham, “Classifying very-high-dimensional data with random forests of oblique decision trees,” in Advances in Knowledge Discovery and Management, F. Guillet, G. Ritschard, D. A. Zighed, and H. Briand, Eds., vol. 292 of Studies in Computational Intelligence, pp. 39–55, Springer, Berlin, Heidelberg, 2010. View at Publisher · View at Google Scholar · View at Scopus
20. N. Poona, A. van Niekerk, and R. Ismail, “Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data,” Sensors, vol. 16, no. 11, 2016. View at Publisher · View at Google Scholar · View at Scopus
21. T. D. Center, “Biological control of weeds: water hyacinth and water lettuce,” in Pest Management in the Subtropics: Biological Control — A Florida Perspective, D. Rosen, F. D. Bennet, and J. L. Capinera, Eds., pp. 481–521, Intercept Publishing Company, Andover, UK, 1994. View at Google Scholar
22. M. H. Julien, “Biological control of water hyacinth with arthropods: a review to 2000,” in Proceedings of the Second Meeting of the Global Working Group for the Biological and Integrated Control of Water Hyacinth, pp. 8–20, Beijing, China, October 2001.
23. S. Lowe, M. Browne, S. Boudjelas, and M. De Poorter, 100 of the World’s Worst Invasive Alien Species: A Selection from the Global Invasive Species Database, The Invasive Species Specialist Group (ISSG) a Specialist Group of the Species Survival Commission (SSC) of the World Conservation Union (IUCN), Auckland, New Zealand, 2004.
24. N. H. Agjee, R. Ismail, and O. Mutanga, “Identifying relevant hyperspectral bands using Boruta: a temporal analysis of water hyacinth biocontrol,” Journal of Applied Remote Sensing, vol. 10, no. 4, article 042002, 2016. View at Publisher · View at Google Scholar · View at Scopus
25. T. D. Center, F. A. Dray Jr., G. P. Jubinsky, and M. J. Grodowitz, “Biological control of water hyacinth under conditions of maintenance management: can herbicides and insects be integrated?” Environmental Management, vol. 23, no. 2, pp. 241–256, 1999. View at Publisher · View at Google Scholar · View at Scopus
26. A. Bownes, M. P. Hill, and M. J. Byrne, “Assessing density–damage relationships between water hyacinth and its grasshopper herbivore,” Entomologia Experimentalis et Applicata, vol. 137, no. 3, pp. 246–254, 2010. View at Publisher · View at Google Scholar · View at Scopus
27. J. A. Coetzee, M. J. Byrne, and M. P. Hill, “Impact of nutrients and herbivory by Eccritotarsus catarinensis on the biological control of water hyacinth, Eichhornia crassipes,” Aquatic Botany, vol. 86, no. 2, pp. 179–186, 2007. View at Publisher · View at Google Scholar · View at Scopus
28. P. G. Soti and J. C. Volin, “Does water hyacinth (Eichhornia crassipes) compensate for simulated defoliation? Implications for effective biocontrol,” Biological Control, vol. 54, no. 1, pp. 35–40, 2010. View at Publisher · View at Google Scholar · View at Scopus
29. A. Bownes, M. P. Hill, and M. J. Byrne, “Evaluating the impact of herbivory by a grasshopper, Cornops aquaticum (Orthoptera: Acrididae), on the competitive performance and biomass accumulation of water hyacinth, Eichhornia crassipes (Pontederiaceae),” Biological Control, vol. 53, no. 3, pp. 297–303, 2010. View at Publisher · View at Google Scholar · View at Scopus
30. R. A. Goyer and J. D. Stark, “The impact of Neochetina eichhorniae on water hyacinth in Southern Louisiana,” Journal of Aquatic Plant Management, vol. 22, pp. 57–61, 1984. View at Google Scholar
31. M. O. Bashir, Z. E. El Abjar, and N. S. Irving, “Observations on the effect of the weevils Neochetina eichhorniae Warner and Neochetina bruchi Hustache on the growth of water hyacinth,” Hydrobiologia, vol. 110, no. 1, pp. 95–98, 1984. View at Publisher · View at Google Scholar · View at Scopus
32. ASD, Handheld Spectroradiometer: User’s Guide Version 4.05, Analytical Spectral Devices Incorporated, Boulder, CO, USA, 2005.
33. R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2012.
34. R. Díaz-Uriarte and S. Alvarez de Andrés, “Gene selection and classification of microarray data using random forest,” BMC Bioinformatics, vol. 7, no. 1, p. 3, 2006. View at Publisher · View at Google Scholar · View at Scopus
35. A. Liaw and M. Wiener, Random Forest: Breiman and Cutler’s Random Forests for Classification and Regression, R Package Version 4.6-7, 2013.
36. E. A. Addink, S. M. de Jong, and E. J. Pebesma, “The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery,” Photogrammetric Engineering & Remote Sensing, vol. 73, no. 8, pp. 905–912, 2007. View at Publisher · View at Google Scholar · View at Scopus
37. J. Hernandez, G. Lobos, I. Matus, A. del Pozo, P. Silva, and M. Galleguillos, “Using ridge regression models to estimate grain yield from field spectral data in bread wheat (Triticum Aestivum L.) grown under three water regimes,” Remote Sensing, vol. 7, no. 2, pp. 2109–2126, 2015. View at Publisher · View at Google Scholar · View at Scopus
38. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY, USA, 1995. View at Publisher · View at Google Scholar
39. G. Mountrakis, J. Im, and C. Ogole, “Support vector machines in remote sensing: a review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247–259, 2011. View at Publisher · View at Google Scholar · View at Scopus
40. B. Menze and N. Splitthoff, Oblique Random Forests from Recursive Linear Model Splits, R Package Version 0.3, 2012.
41. R. G. Congalton, “A review of assessing the accuracy of classifications of remotely sensed data,” Remote Sensing of Environment, vol. 37, no. 1, pp. 35–46, 1991. View at Publisher · View at Google Scholar · View at Scopus
42. G. M. Foody, “Thematic map comparison,” Photogrammetric Engineering & Remote Sensing, vol. 70, no. 5, pp. 627–633, 2004. View at Publisher · View at Google Scholar
43. S. Adelabu, O. Mutanga, E. Adam, and R. Sebego, “Spectral discrimination of insect defoliation levels in mopane woodland using hyperspectral data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 1, pp. 177–186, 2014. View at Publisher · View at Google Scholar · View at Scopus
44. V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, pp. 93–104, 2012. View at Publisher · View at Google Scholar · View at Scopus
45. R. Ross and J. Kelleher, “A comparative study of the effect of sensor noise on activity recognition models,” International Joint Conference on Ambient Intelligence, vol. 413, pp. 151–162, 2013. View at Publisher · View at Google Scholar · View at Scopus