Detection of Lead Pollution in Rice Using Hyperspectral Data

Dong Fan Piao; Wei Hong Zhu; Cheng Zhe Xu

doi:10.4028/www.scientific.net/AMM.303-306.685

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 303-306Detection of Lead Pollution in Rice Using...

Detection of Lead Pollution in Rice Using Hyperspectral Data

Abstract:

This paper presents a new method for detecting lead pollution in rice by using hyperspectral data. First, some transform methods are used to generate relatively independent datasets on hyperspectral data. Then, the kernel discriminant analysis is utilized to extract features from each represented dataset. Finally, new fused features are made by combining aforementioned features serially. The experimental result based on the proposed method shows the good performance in detecting lead pollution in rice.

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Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

685-688

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.685

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Online since:

February 2013

Authors:

Dong Fan Piao, Wei Hong Zhu, Cheng Zhe Xu

Keywords:

Classification, Feature Extraction, Hyperspectral Data, Lead Pollution

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References

[1] P.S. Hu, H.Q. Zhai, and J.M. Wan: New Characteristics of Rice Production and Quality Improvement in China. Review of China Agricultural Science and Technology Vol. 4, pp.33-39 (2002) (in Chinese).

Google Scholar

[2] G.Y. Chi, X.H. Liu, S.H. Liu, and Z.F. Yang. Studies of Relationships Between Cu Pollution and Spectral Characteristics of Tritiznm Aestivum L. Spectroscopy and Spectral Analysis Vol. 26(7), pp.1272-1276 (2006).

Google Scholar

[3] H.Y. Ren, D.F. Zhuang, J.J. Pan, X.Z. Shi, and H.J. Wang: Hyperspectral Remote Sensing to Monitor Vegetation Stress. Journal of Soils and Sediments Vol. 8(5), pp.323-326 (2008).

DOI: 10.1007/s11368-008-0030-4

Google Scholar

[4] M.L. Liu, X.N. Liu W.C. Ding, and L. Wu: Monitoring Stress Levels on Rice with Heavy Metal Pollution from Hyperspectral Reflectance Data Using Wavelet-Fractal Analysis. International Journal of Applied Earth Observation and Geoinformation Vol. 13, pp.246-255 (2011).

DOI: 10.1016/j.jag.2010.12.006

Google Scholar

[5] I.J. Jolliffe, in: Principal Component Analysis, Springer-Verlag, (2002).

Google Scholar

[6] Raghuveer M. Rao and Ajit S. Bopardikar, in: Wavelet Transforms: Introduction to Theory and Applications, Addison Wesley Longman, Inc., (1998).

Google Scholar

[7] S. Mika, g. Ratsch, J. Weston, B. Scholkopf and K. Muller, Fisher Discriminant Analysis with Kernels. In: IEEE Neural Networks for Signal Processing Workshop, 41-48 (1999).

DOI: 10.1109/nnsp.1999.788121

Google Scholar

[8] Jian Yang, Jing-Yu Yang, David Zhang and Jian-feng Lu: Feature Fusion: Parallel Strategy VS. Serial strategy. Pattern Recognition Vol. 36(6), pp.1369-1381 (2003).

DOI: 10.1016/s0031-3203(02)00262-5

Google Scholar