Incorporation of texture information in a SVM method for classifying salt cedar in western China

References

• Akasheh, O. Z., C. M. U. Neale, and H. Jayanthi. 2008. “Detailed Mapping of Riparian Vegetation in the Middle Rio Grande River Using High Resolution Multi-Spectral Airborne Remote Sensing.” Journal of Arid Environments 72: 1734–1744. doi:10.1016/j.jaridenv.2008.03.014.
   Web of Science ®Google Scholar
• Bernstein, L. S., X. M. Jin, B. Gregor, and S. M. Adler-Golden. 2012. “Quick Atmospheric Correction Code: Algorithm Description and Recent Upgrades.” SPIE Optical Engineering 51 (11): 111719. doi:10.1117/1.OE.51.11.111719.
   Web of Science ®Google Scholar
• Bruno, A., B. Stefano, and S. Massimo. 2006. “Enhanced Gram–Schmidt Spectral Sharpening Based on Multivariate Regression of MS and Pan Data.” IEEE Geoscience and Remote Sensing Symposium 6: 3789–3792.
   Google Scholar
• Camps-Valls, G., and L. Bruzzone. 2005. “Kernel-Based Methods for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 43 (6): 1351–1362. doi:10.1109/TGRS.2005.846154.
   Web of Science ®Google Scholar
• Carter, G. A., K. L. Lucas, G. A. Blossom, C. L. Lassitter, D. M. Holiday, D. S. Mooneyhan, D. R. Fastring, T. R. Holcombe, and J. A. Griffith. 2009. “Remote Sensing and Mapping of Tamarisk along the Colorado River, USA: A Comparative Use of Summer-Acquired Hyperion, Thematic Mapper and Quickbird Data.” Remote Sensing 1: 318–329. doi:10.3390/rs1030318.
   Web of Science ®Google Scholar
• Chu, H. T., L. Ge, A. H. M. Ng, and C. Rizos. 2012. “Application of Genetic Algorithm and Support Vector Machine in Classification of Multisource Remote Sensing Data.” International Journal of Remote Sensing Applications 2 (3): 1–11.
   Google Scholar
• Coburn, C. A., and A. C. B. Roberts. 2004. “A Multiscale Texture Analysis Procedure for Improved Forest Stand Classification.” International Journal of Remote Sensing 25: 4287–4308. doi:10.1080/0143116042000192367.
   Web of Science ®Google Scholar
• Cortes, C., and V. Vapnik. 1995. “Support Vector Networks.” Machine Learning 20 (3): 273–297. doi:10.1007/BF00994018.
   Web of Science ®Google Scholar
• Ding, F., and P. Y. Fan. 2011. “Integration of Spectral Indices, Digital Elevation Data and Support Vector Machines for Land Use Classification in Hilly Areas.” Paper published in 2011 International Symposium on Image and Data Fusion (ISIDF), Tengchong, Yunnan, August 9–11, 1–4.
   Google Scholar
• Foody, G. M., and A. Mathur. 2004. “A Relative Evaluation of Multiclass Image Classification by Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42: 1335–1343. doi:10.1109/TGRS.2004.827257.
   Web of Science ®Google Scholar
• Frazier, A. E., and L. Wang. 2011. “Characterizing Spatial Patterns of Invasive Species Using Sub-Pixel Classifications.” Remote Sensing of Environment 115: 1997–2007. doi:10.1016/j.rse.2011.04.002.
   Web of Science ®Google Scholar
• Gong, J. D., G. D. Cheng, X. Y. Zhang, H. L. Xiao, and X. Y. Li. 2002. “Environmental Changes of Ejina Region in the Lower Reaches of Heihe River.” Advances in Earth Science 17 (4): 491–496.
   Google Scholar
• Groeneveld, D., and R. Watson. 2008. “Near‐Infrared Discrimination of Leafless Saltcedar in Wintertime Landsat TM.” International Journal of Remote Sensing 29 (12): 3577–3588. doi:10.1080/01431160701711078.
   Web of Science ®Google Scholar
• Hamada, Y., D. Stow, L. Coulter, J. Jafolla, and L. Hendricks. 2007. “Detecting Tamarisk Species (Tamarix spp.) in Riparian Habitats of Southern California Using High Spatial Resolution Hyperspectral Imagery.” Remote Sensing of Environment 109 (2): 237–248. doi:10.1016/j.rse.2007.01.003.
   Web of Science ®Google Scholar
• Haralick, R. M. 1979. “Statistical and Structural Approaches to Texture.” Proceedings of the IEEE 67 (5): 786–804. doi:10.1109/PROC.1979.11328.
   Web of Science ®Google Scholar
• Hsu, C. W., C. C. Chang, and C. J. Lin. 2003. A Practical Guide to Support Vector Classification. Technical report. Taipei: Department of Computer Science, National Taiwan University.
   Google Scholar
• Huang, C., L. S. Davis, and J. R. G. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23: 725–749. doi:10.1080/01431160110040323.
   Web of Science ®Google Scholar
• Jia, L., H. Shang, G. Hu, and M. Menenti. 2011. “Phenological Response of Vegetation to Upstream River Flow in the Heihe Rive Basin by Time Series Analysis of MODIS Data.” Hydrology and Earth System Sciences 15: 1047–1064. doi:10.5194/hess-15-1047-2011.
   Web of Science ®Google Scholar
• Li, Y. Y., Y. Y. Song, B. Chen, J. X. Zhang, Y. F. Sun, and S. F. Huang. 2011. “Spectral Features of Typical Shrubberies in Welfare Forests in Desert Areas.” Journal of Northwest Forestry University 26 (1): 155–159.
   Google Scholar
• Liu, Q. S., G. H. Liu, L. Yao, and C. Y. Song. 2009. “Field Spectral Features and Constructive Species Classification of Vegetation in Modern Yellow River Delta Using Landsat TM.” Remote Sensing Information 5: 82–86.
   Google Scholar
• Melgani, F., and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42: 1778–1790. doi:10.1109/TGRS.2004.831865.
   Web of Science ®Google Scholar
• Mondal, A., S. Kundu, S. K. Chandniha, R. Shukla, and P. K. Mishra. 2012. “Comparison of Support Vector Machine and Maximum Likelihood Classification Technique Using Satellite Imagery.” International Journal of Remote Sensing and GIS 1 (2): 116–123.
   Google Scholar
• Morisette, J., C. Jarnevich, A. Ullah, W. Cai, J. Pedelty, J. Gentle, T. J. Stohlgren, and J. L. Schnase. 2006. “A Tamarisk Habitat Suitability Map for the Continental United States.” Frontiers in Ecology and the Environment 4 (1): 11–17. doi:10.1890/1540-9295(2006)004[0012:ATHSMF]2.0.CO;2.
   Web of Science ®Google Scholar
• Pacifici, F., M. Chini, and W. J. Emery. 2009. “A Neural Network Approach Using Multi-Scale Textural Metrics from Very High-Resolution Panchromatic Imagery for Urban Land-Use Classification.” Remote Sensing of Environment 113: 1276–1292. doi:10.1016/j.rse.2009.02.014.
   Web of Science ®Google Scholar
• Rouse, J. W. 1974. Monitoring the Vernal Advancement and Retrogradation of Natural Vegetation. Greenbelt, MD: NASA/GSFC, Type III, Final Report, 371.
   Google Scholar
• Shah, C. A., P. Watanachaturaporn, P. K. Varshney, and M. K. Arora. 2003. “Some Recent Results on Hyperspectral Image Classification.” Paper published in 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Washington, DC, October 27–28, 346–353.
   Google Scholar
• Shannon, C. E., and W. Weaver. 1949. A Mathematical Theory of Communication. Urbana: University of Illinois Press.
   Google Scholar
• Silván-Cárdenas, J., and L. Wang. 2010. “Retrieval of Subpixel Tamarix Canopy Cover from Landsat Data along the Forgotten River Using Linear and Nonlinear Spectral Mixture Models.” Remote Sensing of Environment 114 (8): 1777–1790. doi:10.1016/j.rse.2010.04.003.
   Web of Science ®Google Scholar
• Vapnik, V. 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.
   Google Scholar
• Wang, L., J. Silván-Cárdenas, J. Yang, and A. E. Frazier. 2013. “Invasive Saltcedar (Tamarix spp.) Distribution Mapping Using Multiresolution Remote Sensing Imagery.” The Professional Geographer 65 (1): 1–15. doi:10.1080/00330124.2012.679440.
   Web of Science ®Google Scholar
• Wood, E. M., A. M. Pidgeon, V. C. Radeloff, and N. S. Keuler. 2012. “Image Texture as a Remotely Sensed Measure of Vegetation Structure.” Remote Sensing of Environment 121: 516–526. doi:10.1016/j.rse.2012.01.003.
   Web of Science ®Google Scholar
• Zhang, C., and S. E. Franklin. 2002. “Forest Structure Classification Using Airborne Multi-Spectral Image Texture and Kriging Analysis.” Institute of Electrical and Electronics Engineers 4: 2522–2524.
   Google Scholar
• Zhang, D. Y., B. R. Pan, and L. K. Yin. 2003. “The Photogeographical Studies of Tamarix (Tamaricaceae).” Acta Botanica Yunnanica 25 (4): 415–427.
   Google Scholar
• Zhang, H., C. Y. Zhao, B. Zhang, and S. Z. Peng. 2011. “The Application of High Resolution Satellite Imagery Geoeye-1 on the Biomass Estimation of Tamarix ramosissima in Lower Reachs of Heigh River Basin.” Remote Sensing Technology and Application 26 (6): 713–718.
   Google Scholar
• Zhu, G. B., and D. G. Blumberg. 2002. “Classification Using ASTER Data and SVM Algorithms; the Case Study of Beer Sheva, Israel.” Remote Sensing of Environment 80: 233–240. doi:10.1016/S0034-4257(01)00305-4.
   Web of Science ®Google Scholar