A support vector machine classifier based on a new kernel function model for hyperspectral data

References

• Bai, P., X. Zhang, B. Zhang, Y. Li, W. Xie, and J. Liu. 2008. Support Vector Machine and its Application in Mixed Gas Infrared Spectrum Analysis. Xian: Xidian University Publisher.
   Google Scholar
• Chen, Y., X. Zhao, and Z. Lin. 2014. “Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (4): 1295–1305. doi:10.1109/JSTARS.2014.2307356.
   Web of Science ®Google Scholar
• Chi, M., R. Feng, and L. Bruzzone. 2008. “Classification of Hyperspectral Remote-Sensing Data with Primal SVM for Small-Sized Training Dataset Problem.” Advances in Space Research 41 (11): 1793–1799. doi:10.1016/j.asr.2008.02.012.
   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
• Cristianini, N., and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines and Other Kenel-based Learning Methods. Cambridge: Cambridge University Press.
   Google Scholar
• Deng, N., and Y. Tian. 2004. A New Method of Data Ming: Support Vector Machines. Beijing: Science Press.
   Google Scholar
• Foody, G. M., and A. Mathur. 2006. “The Use of Small Training Sets Containing Mixed Pixels for Accurate Hard Image Classification: Training on Mixed Spectral Responses for Classification by a SVM.” Remote Sensing of Environment 103 (2): 179–189. doi:10.1016/j.rse.2006.04.001.
   Web of Science ®Google Scholar
• Goodenough, D. G., A. Dyk, K. O. Niemann, J. S. Pearlman, H. Chen, T. Han, M. Murdoch, and C. West. 2003. “Processing Hyperion and ALI for Forest Classification.” IEEE Transactions on Geoscience and Remote Sensing 41 (6): 1321–1331. doi:10.1109/TGRS.2003.813214.
   Web of Science ®Google Scholar
• Guneralp, I., A. M. Filippi, and B. U. Hales. 2013. “River-Flow Boundary Delineation from Digital Aerial Photography and Ancillary Images Using Support Vector Machines.” GIScience & Remote Sensing 50 (1): 1–25. doi:10.1080/15481603.2013.778560.
   Web of Science ®Google Scholar
• Hasanlou, M., F. Samadzadegan, and S. Homayouni. 2015. “SVM-Based Hyperspectral Image Classification Using Intrinsic Dimension.” Arab J Geosci 8: 477–487. doi:10.1007/s12517-013-1141-9.
   Web of Science ®Google Scholar
• Heiden, U., K. Segl, S. Roessner, and H. Kaufmann. 2007. “Determination of Robust Spectral Features for Identification of Urban Surface Materials in Hyperspectral Remote Sensing Data.” Remote Sensing of Environment 111 (4): 537–552. doi:10.1016/j.rse.2007.04.008.
   Web of Science ®Google Scholar
• Jian, Y., and Q. Zhao. 2009. Machine Learning Techniques. Beijing: Publishing House of Electronics Industry.
   Google Scholar
• Kim, Y. H., J. Im, H. K. Ha, J. Choi, and S. Ha. 2014. “Machine Learning Approaches to Coastal Water Quality Monitoring Using GOCI Satellite Data.” GIScience & Remote Sensing 51 (2): 158–174. doi:10.1080/15481603.2014.900983.
   Web of Science ®Google Scholar
• Kumar, M., and I. N. Kar. 2009. “Non-Linear HVAC Computations Using Least Square Support Vector Machines.” Energy Conversion and Management 50 (6): 1411–1418. doi:10.1016/j.enconman.2009.03.009.
   Web of Science ®Google Scholar
• Li, M., J. Im, and C. Beier. 2013. “Machine Learning Approaches for Forest Classification and Change Analysis Using Multi-Temporal Landsat TM Images over Huntington Wildlife Forest.” GIScience & Remote Sensing 50 (4): 361–384. doi:10.1080/15481603.2013.819161.
   Web of Science ®Google Scholar
• Linden, S. V. D., and P. Hostert. 2009. “The Influence of Urban Structures on Impervious Surface Maps from Airborne Hyperspectral Data.” Remote Sensing of Environment 113 (11): 2298–2305. doi:10.1016/j.rse.2009.06.004.
   Web of Science ®Google Scholar
• Lu, D., G. Li, E. Moran, and W. Kuang. 2014. “A Comparative Analysis of Approaches for Successional Vegetation Classification in the Brazilian Amazon.” GIScience & Remote Sensing 51 (6): 695–709. doi:10.1080/15481603.2014.983338.
   Web of Science ®Google Scholar
• Maxwell, A. E., M. P. Strager, T. A. Warner, N. P. Zegre, and C. B. Yuill. 2014. “Comparison of NAIP Orthophotography and RapidEye Satellite Imagery for Mapping of Mining and Mine Reclamation.” GIScience & Remote Sensing 51 (3): 301–320. doi:10.1080/15481603.2014.912874.
   Web of Science ®Google Scholar
• Moreira, L. C., A. D. S. Teixeira, and L. S. Galvao. 2015. “Potential of Multispectral and Hyperspectral Data to Detect Saline-Exposed Soils in Brazil.” GIScience & Remote Sensing 52 (4): 416–436. doi:10.1080/15481603.2015.1040227.
   Web of Science ®Google Scholar
• Mountrakis, G., J. Im, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
   Web of Science ®Google Scholar
• Pignatti, S., R. M. Cavalli, V. Cuomo, L. Fusilli, S. Pascucci, M. Poscolieri, and F. Santini. 2009. “Evaluating Hyperion Capability for Land Cover Mapping in a Fragmented Ecosystem: Pollino National Park, Italy.” Remote Sensing of Environment 113 (3): 622–634. doi:10.1016/j.rse.2008.11.006.
   Web of Science ®Google Scholar
• Plaza, A., J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, et al. 2009. “Recent Advances in Techniques for Hyperspectral Image Processing.” Remote Sensing of Environment 113 (1): S110–S122. doi:10.1016/j.rse.2007.07.028.
   Web of Science ®Google Scholar
• Rao, C. V., J. M. Rao, A. S. Kumar, B. Lakshmi, and V. K. Dadhwal. 2015. “Expansion of LISS III Swath Using AWiFS Wider Swath Data and Contourlet Coefficients Learning.” GIScience & Remote Sensing 52 (1): 78–93. doi:10.1080/15481603.2014.983370.
   Web of Science ®Google Scholar
• Rosa, D. L., and D. Wiesmann. 2013. “Land Cover and Impervious Surface Extraction Using Parametric and Non-Parametric Algorithms from the Open-Source Software R: An Application to Sustainable Urban Planning in Sicily.” GIScience & Remote Sensing 50 (2): 231–250. doi:10.1080/15481603.2013.795307.
   Web of Science ®Google Scholar
• Scholkopf, B., C. J. C. Burges, and A. J. Smola. 1998. Advances in Kernel Methods: Support Vector Learning. Cambridge: MIT Press.
   Google Scholar
• Sengur, A. 2009. “Multiclass Least-Squares Support Vector Machines for Analog Modulation Classification.” Expert Systems with Applications 36 (3): 6681–6685. doi:10.1016/j.eswa.2008.08.066.
   Web of Science ®Google Scholar
• Shao, Z., L. Zhang, X. Zhou, and L. Ding. 2014. “A Novel Hierarchical Semisupervised SVM for Classification of Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters 11 (9): 1609–1613. doi:10.1109/LGRS.2014.2302034.
   Web of Science ®Google Scholar
• Shawe-Taylor, J., and N. Cristianini. 2004. Kernel Methods for Pattern Analysis. London: Cambridge University Press.
   Google Scholar
• Ungar, S. G., J. S. Pearlman, J. A. Mendenhall, and D. Reuter. 2003. “Overview of the Earth Observing One (EO-1) Mission.” IEEE Transactions on Geoscience and Remote Sensing 41 (6): 1149–1159. doi:10.1109/TGRS.2003.815999.
   Web of Science ®Google Scholar
• Vapnik, V. N. 2000. The Nature of Statistical Learning Theory. New York, NY: Springer.
   Google Scholar
• Wang, G. 2006. “Properties and Construction Methods of Kernel in Support Vector Machine.” Computer Science 33 (6): 172–178.
   Google Scholar
• Wu, P. 2001. “From Earth Observing-1 Satellite to See the New Satellite Technology in the 21st Century.” International Space 8: 10–16.
   Google Scholar
• Xun, L., and L. Wang. 2015. “An Object-Based SVM Method Incorporating Optimal Segmentation Scale Estimation Using Bhattacharyya Distance for Mapping Salt Cedar (Tamarisk spp.) with QuickBird Imagery.” GIScience & Remote Sensing 52 (3): 257–273. doi:10.1080/15481603.2015.1026049.
   Web of Science ®Google Scholar
• Zhang, X. 2000. “Introduction to Statistical Learning Theory and Support Vector Machines.” Acta Automatica Sinica 26 (1): 32–42.
   Google Scholar
• Zhou, L., K. K. Lai, and L. Yu. 2010. “Least Squares Support Vector Machines Ensemble Models for Credit Scoring.” Expert Systems with Applications 37 (1): 127–133. doi:10.1016/j.eswa.2009.05.024.
   Web of Science ®Google Scholar