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Comparison of the Different Classifiers in Vegetation Species Discrimination Using Hyperspectral Reflectance Data

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Abstract

Feature selection methods play an important role in Hyperspectral Remote Sensing applications, especially in classification. This paper proposed a new Feature selection strategy for Hyperspectral dataset. This strategy was designed to help refine vegetation classification of 4 categories with 13 species vegetation which are the most common species in central China. An ASD field spectrometer (Analytical Spectral Device) was used to collect spectrum information of plant leaves from each species through 400 nm to 900 nm with 1 nm spectral resolution. Firstly, correlation between the physical/chemical characteristics of the leaves and the separability of each vegetation species was tested. Then, two feature selection methods, spectral angle and spectral distance, and the feature parameters extracted from spectral curves (FPESC) were used to build the feature space which would be the input space for the classifiers. At last, two linear classifiers, mahalanobis distance (MDC), and fisher linear discriminate analysis (FLDA), and a quadratic classifier, maximum likelihood (MLC), were used for vegetation species refine classification. The results showed that (1) there were no significant differences among 13 species on the leaf dry weight (physical parameter) and leaf chlorophyll content (chemical parameter); (2) FPESC of 13 species have distinctive differences and could be ideal features to discriminate these species; (3) The linear classifiers, MDC and FLDA, have better classification results in the experiments compared to the quadratic classifier MLC, where MDC has the highest classification accuracy which is above 96.2 %.

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References

  • Bajorski, P. (2011). Statistical inference in PCA for hyperspectral images. IEEE Journal of Selected Topics in Signal Processing, 5(3), 438–445.

    Article  Google Scholar 

  • Bishop, C. M. (2006). Pattern recognition and machine learning. Printed in Singapore.

  • Chen, E. X., Li, Z. Y., Tan, B. X., Liang, Y. Z., & Zhang, Z. L. (2007). Validation of statistic based forest types classification methods using hyerspectral data. Scientia Silvae Sinicae, 43(1), 84–89.

    Google Scholar 

  • Clark, M. L., Roberts, D. A., & Clark, D. B. (2005). Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96(3–4), 375–398.

    Article  Google Scholar 

  • Cochrane, M. A. (2000). Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing, 21(10), 2075–2087.

    Article  Google Scholar 

  • Gong, P., Pu, R. L., & Yu, B. (1998). Conifer species recognition with seasonal hyperspectral data. Journal of Remote Sensing, 2(3), 211–217.

    Google Scholar 

  • Goodenough, D. G., Niemann, A., Dyk, K. O., Pearlman, J. S., Chen, H., Han, T., et al. (2003). Processing hyperion and ALI for forest classification. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1321–1331.

    Article  Google Scholar 

  • Hestir, E. L., Khanna, S., Andrew, M. E., Santos, M. J., Viers, J. H., Greenberg, J. A., et al. (2008). Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sensing of Environment, 112(11), 4034–4047.

    Article  Google Scholar 

  • Hoffbeck, J. P., & Landgrebe, D. A. (1996). Covariance matrix estimation and classification with limited training data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 763–767.

    Article  Google Scholar 

  • Huang, C. Y., & Asner, G. P. (2009). Applications of remote sensing to alien invasive plant studies. Sensors, 9, 4869–4889.

    Article  Google Scholar 

  • Ismail, M. H., & Jusoff, K. (2008). Satellite data classification accuracy assessment based from a reference data set. International Journal of Computer and Information Science Engineering, 2, 96–102.

    Google Scholar 

  • Jon, A. B., Martino, P., & Kolbeinn, A. (2003). Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, 41(9), 1940–1949.

    Article  Google Scholar 

  • Jose, P. H., & David, A. L. (1996). Classification of remote sensing image having high spectral resolution. Remote Sensing of Environment, 57, 119–126.

    Article  Google Scholar 

  • Jouan, A. (2007). FastICA (MNF) for feature generation in hyperspectral imagery. 10th International Conference on Information Fusion, 1–8.

  • Kalacska, M., Bohlman, S., Sanchez-Azofeifa, G. A., Castro-Esau, K., & Caelli, T. (2007). Hyperspectral discrimination of tropical dry forest lianas and trees: comparative data reduction approaches at the leaf and canopy levels. Remote Sensing of Environment, 109(4), 406–415.

    Article  Google Scholar 

  • Kozoderov, V. V., & Dmitriev, E. V. (2011). Remote sensing of soils and vegetation: quantitative parameters retrieval using pattern-recognition techniques and forest stand structure assessment. International Journal of Remote Sensing, 32(20), 5699–5717.

    Article  Google Scholar 

  • Lu, D. (2005). Integration of vegetation inventory data and Landsat TM image for vegetation classification in the western Brazilian Amazon. Forest Ecology and Management, 213, 369–383.

    Article  Google Scholar 

  • Lu, D. S., Batistella, M., Moran, E., & de Miranda, E. E. (2008). A comparative study of Landsat TM and SPOT HRG images for vegetation classification in the Brazilian Amazon. Photogrammetric Engineering and Remote Sensing, 74(3), 311–321.

    Article  Google Scholar 

  • Martin, M. E. (1998). Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment, 65, 249–254.

    Article  Google Scholar 

  • Onojeghuo, A. O., & Blackburn, G. A. (2011). Mapping reedbed habitats using texture-based classification of QuickBird imagery. International Journal of Remote Sensing, 32(23), 8121–8138.

    Article  Google Scholar 

  • Perumal, K., & Bhaskaran, R. (2010). Supervised classification performance of multispectral images. Journal of Computing, 2(2), 124–129.

    Google Scholar 

  • Pudil, P., Novevicova, J., & Kittler, J. (1994). Floating search methods in feature selection. Pattern Recognition Letters, 15(11), 1119–1125.

    Article  Google Scholar 

  • Serpico, S. B., & Bruzzone, L. (2001). A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1360–1367.

    Article  Google Scholar 

  • Smith, K. L., Steven, M. D., & Colls, J. J. (2004). Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sensing of Environment, 92, 207–217.

    Article  Google Scholar 

  • Thenkabail, P. S., Enclona, E. A., Ashton, M. S., & Meer, B. V. D. (2004). Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91(3–4), 354–376.

    Article  Google Scholar 

  • Vaiphasa, C., Ongsomwang, S., Vaiphasa, T., & Skidmore, A. K. (2005). Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuarine, Coastal and Shelf Science, 65, 371–379.

    Article  Google Scholar 

  • Vaiphasa, C., Skidmore, A. K., Boer, W. F., & Vaiphasa, T. (2007). A hyperspectral band selector for plant species discrimination. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 225–235.

    Article  Google Scholar 

  • Wang, L., Ji, H. B., & Shi, Y. (2011). Face recognition using maximum local fisher discriminant analysis, 18th IEEE International Conference on Image Processing, 1737–1740.

  • Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology, 1, 9–23.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Pushnik, J. C., Dobrowski, S., & Ustin, S. L. (2003). Steady state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84, 283–294.

    Article  Google Scholar 

  • Zaw Htun, N., Mizoue, N., & Yoshida, S. (2011). Classifying tropical deciduous vegetation: a comparison of multiple approaches in Popa Mountain Park, Myanmar. International Journal of Remote Sensing, 32(24), 8935–8948.

    Article  Google Scholar 

  • Zhang, J., Rivard, B., Sánchez-Azofeifa, A., & Castro-Esau, K. (2006). Intra-and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: implications for species identification using HYDICE imagery. Remote Sensing of Environment, 105(2), 129–141.

    Article  Google Scholar 

  • Zhao, D., Huang, L., Li, J., & Qi, J. (2007). A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. ISPRS Journal of Photogrammetry and Remote Sensing, 62(1), 25–33.

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by National High Technology Research and Development Program of China (863 Program)(grand No. 2012AA12A304 and 2013AA102401). Additional funding and supporting is also provided by the Fundamental Research Funds for the Central Universities (grand No.2012ZYTS037).

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Authors and Affiliations

  1. College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China

    Yuanyong Dian, Yongrong Xu & Chonghuai Yao

  2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Shenghui Fang & Yuan Le

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  1. Yuanyong Dian
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  2. Shenghui Fang
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  3. Yuan Le
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  4. Yongrong Xu
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  5. Chonghuai Yao
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Correspondence to Yuanyong Dian.

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Dian, Y., Fang, S., Le, Y. et al. Comparison of the Different Classifiers in Vegetation Species Discrimination Using Hyperspectral Reflectance Data. J Indian Soc Remote Sens 42, 61–72 (2014). https://doi.org/10.1007/s12524-013-0309-9

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  • DOI: https://doi.org/10.1007/s12524-013-0309-9

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