Abstract
Forest precision classification products were the basic data for surveying of forest resource, updating forest subplot information, logging and management of forest. However, due to the diversity of stand structure, complexity of the forest growth environment, it is difficult to discriminate forest tree species using multi-spectral image. The airborne hyper-spectral images can obtain high spatial and spectral resolution imagery of forest canopy, so it may be useful for tree species level classification. The aim of this paper was to test the effective of combining spatial and spectral features in airborne hyper-spectral image classification. The CASI hyper spectral image data were acquired from Liangshui natural reserves area. First the MNF (minimum noise fraction) transform method for to reduce the hyperspectral image dimensionality and highlighting variation. Second, the grey level co-occurrence matrix (GLCM) is used to extract the texture features of forest tree canopy. Thirdly the texture and the spectral features of forest canopy were fused to classify the trees species using support vector machine (SVM) with different kernel functions. The results showed that when using the SVM classifier, MNF and texture-based features combined with linear kernel function can achieve the best overall accuracy which was 85.92 %. It also confirmed the belief that combined the spatial and spectral information can improve the accuracy of tree species classification.
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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.
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.
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.
Gong, P., Pu, R. L., & Yu, B. (1998). Conifer species recognition with seasonal hyperspectral data. Journal of Remote Sensing, 2(3), 211–217.
Green, A. A., Berman, M., Switzer, P., & Craig, M. D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal,”. IEEE Transaction on Geoscience and Remote Sensing, GRS-26(1), 65–74.
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.
Hsu, C.W., Chang, C. C., & Lin, C. J. (2001). A practical guide to support vector classification
Huang, C. Y., & Asner, G. P. (2009). Applications of remote sensing to alien invasive plant studies. Sensors, 9, 4869–4889.
Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with random forest using very high spatial resolution 8-band worldview-2 satellite data. Remote Sensing, 4, 2661–2693.
Jose, P. H., & David, A. L. (1996). Classification of remote sensing image having high spectral resolution. Remote Sensing of Environment, 57, 119–126.
Jouan, A. (2007). FastICA(MNF) for feature generation in hyperspectral imagery, 10th International Conference on Information Fusion, 1–8
Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with gaussian kernel. Neural Computation, 15(7), 1667–1689.
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.
Leempoel, K., Bourgeois, C., Zhang, J., Wang, J., Chen, M., Satyaranayana, B., Bogaert, J., & Dahdouh-Guebas, F. (2013). Spatial heterogeneity in mangroves assessed by GeoEye-1 satellite data: a case-study in Zhanjiang Mangrove National Nature Reserve (ZMNNR). China, Biogeosciences Discussion, 10, 2591–2615.
Liang, Y. Q., & Zeng, H. (2009). Application of hyperspectral remote sensing in identification of vegetation characteristics. World Forestry Research, 22(1), 41–47.
Lin, H. T., & Lin, C. J. (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. National Taiwan University: Technical report, Department of Computer Science.
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.
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.
Nielsen, A. A. (2011). Kernel maximum autocorrelation factor and minimum noise fraction transformations. IEEE Transaction on Geoscience and Remote Sensing, 20(3), 612–624.
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.
Sedano, F., Gong, P., & Ferrao, M. (2005). Land cover assessment with MODIS imagery in southern African miombo ecosystems. Remote Sensing of Environment, 98(4), 429–441.
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 Environment, 92, 207–17.
Ustin, Susan, L., John, A., & Gamon. (2010). Remote sensing of plant functional types. New Phytologist, 186(4), 795–816.
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.
Vapnik, V. N. (1998). Statistical Learning Theory. Hoboken: Wiley.
Wang, K., Steven, E., Franklin, X. G., & Cattet, M. (2010). Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists. Sensors, 10(11), 9647–9667.
White, J. C., Gómez, C., Wulder, M. A., & Coops, N. C. (2010). Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data. Remote Sensing of Environment, 114, 1576–1589.
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.
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, 129–141.
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.
Acknowledgments
This research was supported by National High Technology Research and Development Program of China (863 Program) (grant No. 2012AA120906). Additional funding and supporting were also provided by the Fundamental Research Funds for the Central Universities (grant No. 2014QC018) and Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (grant No. 2013NGCM05)
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Dian, Y., Li, Z. & Pang, Y. Spectral and Texture Features Combined for Forest Tree species Classification with Airborne Hyperspectral Imagery. J Indian Soc Remote Sens 43, 101–107 (2015). https://doi.org/10.1007/s12524-014-0392-6
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DOI: https://doi.org/10.1007/s12524-014-0392-6