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

Journal of the Indian Society of Remote Sensing

Volumn 42, Issue 1, 2014, Pages 61-72

  Dian, Yuanyong ^a   Fang, Shenghui ^b   Le, Yuan ^b   Xu, Yongrong ^a   Yao, Chonghuai ^a  

^a HUAZHONG AGRICULTURAL UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Feature selection; Fisher linear classifier; Hyperspectral reflectance; Mahalanobis Distance; Maximum likelihood classifier; Spectral discriminate analysis; Vegetation species discriminate

Indexed keywords

ACCURACY ASSESSMENT; CHLOROPHYLL; MAXIMUM LIKELIHOOD ANALYSIS; REMOTE SENSING; SPECTRAL REFLECTANCE; SPECTRAL RESOLUTION; SPECTROMETER; VEGETATION CLASSIFICATION;

CHINA;

EID: 84896382970     PISSN: 0255660X     EISSN: 09743006     Source Type: Journal    
DOI: 10.1007/s12524-013-0309-9     Document Type: Article

References (33)

1. Bajorski, P. (2011). Statistical inference in PCA for hyperspectral images. IEEE Journal of Selected Topics in Signal Processing, 5(3), 438-445.
2. Bishop, C. M. (2006). Pattern recognition and machine learning. Printed in Singapore.
3. 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.
4. 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.
5. Cochrane, M. A. (2000). Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing, 21(10), 2075-2087.
6. Gong, P., Pu, R. L., & Yu, B. (1998). Conifer species recognition with seasonal hyperspectral data. Journal of Remote Sensing, 2(3), 211-217.
7. 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.
8. 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.
9. 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.
10. Huang, C. Y., & Asner, G. P. (2009). Applications of remote sensing to alien invasive plant studies. Sensors, 9, 4869-4889.
11. 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.
12. 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.
13. Jose, P. H., & David, A. L. (1996). Classification of remote sensing image having high spectral resolution. Remote Sensing of Environment, 57, 119-126.
14. Jouan, A. (2007). FastICA (MNF) for feature generation in hyperspectral imagery. 10th International Conference on Information Fusion, 1-8.
15. 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.
16. 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.
17. 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.
18. 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.
19. Martin, M. E. (1998). Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment, 65, 249-254.
20. 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.
21. Perumal, K., & Bhaskaran, R. (2010). Supervised classification performance of multispectral images. Journal of Computing, 2(2), 124-129.
22. Pudil, P., Novevicova, J., & Kittler, J. (1994). Floating search methods in feature selection. Pattern Recognition Letters, 15(11), 1119-1125.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. 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.
29. Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology, 1, 9-23.
30. 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.
31. 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.
32. 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.
33. 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.