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Zhang, Yongxin
- Analysis of Worldview-2 Band Importance in Tree Species Classification based on Recursive Feature Elimination
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Authors
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1 School of Land and Tourism, Luoyang Normal University, Luoyang, Henan Province, 471934, CN
2 College of forestry, Inner Mongolia Agricultural University, Huhhot, Inner Mongolia, 010019, CN
1 School of Land and Tourism, Luoyang Normal University, Luoyang, Henan Province, 471934, CN
2 College of forestry, Inner Mongolia Agricultural University, Huhhot, Inner Mongolia, 010019, CN
Source
Current Science, Vol 115, No 7 (2018), Pagination: 1366-1374Abstract
In tree species classifications, different spectral bands feature different importance, and the manner of determining the importance of one band is a problem that needs to be solved. In this study, eight bands of the WorldView-2 fusion data were used as information sources, and a recursive feature elimination based on maximum likelihood (MLC-RFE) was used to sort the importance of these bands. According to the results, the importance of the eight bands was sorted as follows (from important to unimportant): nearinfrared 2 > red edge > yellow > red > near-infrared 1 > coastal blue > green > blue. The poorest band combination yielded the lowest overall accuracy (OA) and Kappa coefficient (40.9153%; 0.3080), whereas the optimal band combination presented the highest OA and Kappa coefficient (74.5479%; 0.7029), indicating the large difference in accuracies between the optimal and poorest band combinations. Therefore, selecting important bands bears significance in tree species classifications. The MLC-RFE method significantly solved the band selection problem. Thus, this method should be extended to more complex feature selections.Keywords
Bands Importance, Maximum Likelihood, Recursive Feature Elimination, Tree Classification, Worldview- 2.References
- Fassnacht, F. E. et al., Review of studies on tree species classification from remotely sensed data. Remote Sens. Environ., 2016, 186(214), 64-87.
- Ferreira, M. P. et al., Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data. Remote Sens. Environ., 2016, 179, 66-78.
- Li, D., Ke, Y., Gong, H. and Li, X., Object-based urban tree species classification using bi-temporal worldview-2 and worldview3 images. Remote Sens-(Basel), 2015, 7(12), 16917-16937.
- Heumann, B. W., An object-based classification of mangroves using a hybrid decision tree-support vector machine approach. Remote Sens-Basel., 2011, 3(11), 2440-2460.
- Immitzer, M., Atzberger, C. and Koukal, T., Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens-(Basel), 2012, 4(9), 2661-2693.
- Immitzer, M., Atzberger, C. and Koukal, T., Suitability of WorldView-2 data for tree species classification with special emphasis on the four new spectral bands. Photogramm. Fernerkun. Geoinf., 2012, 5, 573-588.
- Pu, R. and Landry, S., A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sens. Environ., 2012, 124(9), 516-533.
- Cho, M. A. et al., Mapping tree species composition in South African savannas using an integrated airborne spectral and lidar system. Remote Sens. Environ., 2012, 125(10), 214-226.
- Peerbhay, K. Y., Mutanga, O. and Ismail, R., Investigating the capability of few strategically placed worldview-2 multispectral bands to discriminate forest species in Kwazulu-Natal, South Africa. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2013, 7(1), 307-316.
- Deng, S. et al., Interpretation of forest resources at the individual tree level at purple mountain, nanjing city, china, using worldview-2 imagery by combining GPS, RS and GIS technologies. Remote Sens-(Basel), 2013, 6(1), 87-110.
- Ghosh, A. and Joshi, P. K., A comparison of selected classification algorithms for mapping bamboo patches in lower gangetic plains using very high resolution worldview 2 imagery. Int. J. Appl. Earth Obs. Geoinf., 2014, 26(1), 298-311.
- Guyon, I., Weston, J., Barnhill, S. and Vapnik, V., Gene selection for cancer classification using support vector machines. Mach. Learn., 2002, 46(1-3), 389-422.
- Camps-Valls, G., Gomez-Chova, L., Calpe-Maravilla, J. and Martin-Guerrero, J. D., Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Trans. Geosci. Remote Sens., 2004, 42(7), 1530-1542.
- Bazi, Y. and Melgani, F., Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Trans. Geosci. Remote Sens., 2006, 44(11), 3374-3385.
- Zhang, R. and Ma, J., A feature selection algorithm for hyperspectual data with SVM-RFE. Geomatics Inf. Sci. Wuhan Univ., 2009, 34(7), 834-837.
- Zhang, H., Huang, B. and Yu, L., Kernel function in SVM-RFE based hyperspectral data band selection. Remote Sens. Technol. Appl., 2013, 28(5), 747-752 (in Chinese).
- Liu, H. P., An, H. J., Wang, B. and Zhang, Q. L., Tree species classification using WorldView-2 images based on recursive texture feature elimination. J. Beijing For. Univ., 2015, 37(8), 53-59 (in Chinese).
- Chavez, P. S., Berlin, G. L. and Sowers, L. B., Statistical method for selecting landsat MSS ratios 147. J. Appl. Photogr. Eng., 1982, 8(1), 23-30.
- Qaid, A. M. and Basavarajappa, H. T., Application of optimum index factor technique to landsat7 data for geological mapping of north east of Hajjah, Yemen. Am.-Eurasian J. Sci. Res., 2008, 3(1), 84-91.
- Malhi, A. and Gao, R. X., PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas., 2004, 53(6), 1517-1525.
- Kumaran, N. and Bhavani, R., PCA-based feature selection for MRI image retrieval system using texture features. Artif. Intell. Evol. Algorithms Eng. Syst., Springer India, 2015, 324, 109-117.