참고문헌 (42)

1. J.R. Anderson 28 1976 Geological Survey Professional Paper 964 Anderson, J.R., Hardy E.E., Roach J.T., and Witmer R.E., 1976, A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper 964, US Government Printing Office, Washington, DC, 28 p. 

2. Physical Geography A.F. Barton 38 176 2017 10.1080/02723646.2016.1236606 Barton A.F. and Xue, Y., 2017, Machine learning predictions of passive microwave brightness temperature over snow-covered land using the special sensor microwave imager (SSM/I). Physical Geography, 38, 176-196. 

   상세보기

3. C.M. Bishop 482 1995 Neural networks for pattern recognition Bishop, C.M., 1995, Neural networks for pattern recognition. Oxford University Press, New York, 482 p. 

4. The Egyptian Journal of Remote Sensing and Space Science A. Butt 18 251 2015 10.1016/j.ejrs.2015.07.003 Butt A., Shabbir R., Ahmad S.S., and Aziz N., 2015, Land use change mapping and analysis using remote sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 18, 251-259. 

   상세보기

5. Educational and Psychological Measurement J.A. Cohen 20 37 1960 10.1177/001316446002000104 Cohen, J.A., 1960, A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46. 

   상세보기

6. R. Collobert 160 2008 10.1145/1390156.1390177 Proceedings of the 25th International Conference on Machine learning Collobert, R. and J. Weston., 2008, A unified architecture for natural language processing: deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine learning, Helsinki, Jul. 5-9, p. 160-167. 

   

7. IEEE Transactions on Knowledge and Data Engineering H.H. Dam 20 26 2008 10.1109/TKDE.2007.190671 Dam, H.H., Abbass, H.A., Lokan, C., and Yao, X., 2008, Neural-based learning classifier systems. IEEE Transactions on Knowledge and Data Engineering, 20, 26-39. 

   상세보기

8. S. Ding 1 2009 International Conference on Information Engineering and Computer Science Ding, S. and Chen, L., 2009, Classification of hyperspectral remote sensing images with support vector machines and particle swarm optimization. International Conference on Information Engineering and Computer Science, Wulan, Dec. 19-20, IEEE, p. 1-5. 

9. IEEE Transactions on Geoscience and Remote Sensing J. Ediriwickrema 35 810 1997 10.1109/36.602523 Ediriwickrema, J. and Khorram, S., 1997, Hierarchical maximum-likelihood classification for improved accuracies. IEEE Transactions on Geoscience and Remote Sensing, 35, 810-816. 

   상세보기

10. Catrina Journal M. Elhag 6 11 2010 Elhag, M., 2010, Land suitability for afforestation and nature conservation practices using remote sensing & GIS techniques. Catrina Journal, 6, 11-17. 

11. M. Elhag 2016 Journal of Sensors Elhag, M., 2016b, Evaluation of different soil salinity mapping using remote sensing techniques in arid ecosystems, Saudi Arabia. Journal of Sensors, Article ID 7596175. http://dx.doi.org/10.1155/2016/7596175 

12. Korean Journal of Remote Sensing J. Eom 33 201 2017 Eom, J. and Lee, C.W., 2017, Analysis on the area of deltaic Barrier Island and suspended sediments concentration in Nakdong River using satellite images. Korean Journal of Remote Sensing, 33, 201-211. 

    원문보기 상세보기

13. IEEE Transactions on Geoscience Remote Sensing G.M. Foody 42 1335 2004 10.1109/TGRS.2004.827257 Foody, G.M. and Mathur, A., 2004, A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience Remote Sensing, 42, 1335-1343. 

    상세보기

14. J.H. Fournelle 723 1994 The Geology of Alaska Fournelle, J.H., Marsh, B.D., and Myers, J.D., 1994, Age, character, and significance of Aleutian arc volcanism. In: Plafker, G. and Berg, H.C. (eds.), The Geology of Alaska. Geological Society of America, Geology of North America, Boulder, G-1, p. 723-757. 

15. D.D. Gibson 351 2007 Nuttall Ornthological Club Gibson, D.D. and Byrd, G.V., 2007, Birds of the Aleutian Islands, Alaska. Nuttall Ornthological Club, Cambridge; American Ornithologists’ Union, Washington, 351 p. 

16. Geological Society of America Bulletin J.A. Grow 84 2169 1973 10.1130/0016-7606(1973)84<2169:CAUMSO>2.0.CO;2 Grow, J.A., 1973, Crustal and upper mantle structure of the central Aleutian arc. Geological Society of America Bulletin, 84, 2169-2192. 

    상세보기

17. J.A. Gualtieri 221 1999 The 27th AIPR Workshop: Advances in Computer-Assisted Recognition 10.1117/12.339824 Gualtieri, J.A. and Cromp, R.F., 1999, Support vector machines for hyperspectral remote sensing classification. The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, Washington DC, Jan. 29, International Society for Optics and Photonics, 3584, p. 221-232. 

    

18. Applied Science P.R. Kadavi 7 935 2017 10.3390/app7090935 Kadavi, P.R., Lee, W.J., and Lee, C.W., 2017, Analysis of the pyroclastic flow deposits of Mounts Sinabung and Merapi using Landsat imagery and the artificial neural network approach. Applied Science, 7, 935. http://dx.doi.org/10.3390/app7090935 

    상세보기

19. International Journal of Applied Earth Observation and Geoinformation T. Kavzoglu 11 352 2009 10.1016/j.jag.2009.06.002 Kavzoglu, T. and Colkesen, I., 2009, A kernel functions analysis for Support Vector Machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11, 352-359. 

    상세보기

20. Journal of Geophysical Research S.M. Kay 87 4051 1982 10.1029/JB087iB05p04051 Kay, S.M., Kay, R.W., and Citron, G.P., 1982, Tectonic controls on tholeiitic and calc-alkaline magmatism in the Aleutian arc. Journal of Geophysical Research, 87, 4051-4072. 

    상세보기

21. S. Khorram 65 1999 Accuracy assessment of remote sensing-derived change detection Khorram, S., Biging, G.S., Chrisman, N.R., Colby, D.R., Congalton, R.G., Dobson, J.E., Ferguson, R.L., Goodchild, M.F., Jensen, J.R., and Mace, T.H., 1999, Accuracy assessment of remote sensing-derived change detection. American Society for Photogrammetry and Remote Sensing, Monograph Series, 65 p. 

22. E. Koomen 1 2007 Modelling Land- Use Change: Progress and Applications Koomen, E. and Stillwell, J., 2007, Modelling land-use change. In: Koomen, E., Stillwell, J., Bakema, A., and Scholten, H.J. (eds.), Modelling Land- Use Change: Progress and Applications. Springer Netherlands, p. 1-22. 

23. Korean Journal of Remote Sensing S. Lee 32 639 2016 10.7780/kjrs.2016.32.6.8 Lee, S., Kim, G., and Lee, C.W., 2016, Preliminary study for tidal flat detection in Yeongjong-do according to tide level using Landsat images. Korean Journal of Remote Sensing, 32, 639-645. 

    원문보기 상세보기

24. Remote Sensing C. Li 6 964 2014 10.3390/rs6020964 Li, C., Wang, J., Wang, L., Hu, L., and Gong, P., 2014, Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery. Remote Sensing, 6, 964-983. 

    상세보기

25. 10.1590/S0100-204X2012000900004 Lu, D., Batistella, M., Li, G., Moran, E., Dutra, L.V., Freitas, C.C., and Sant’Anna, S.J., 2012, Land use/cover classification in the Brazilian Amazon using satellite images. Pesquisa Agropecuaria Brasileira, 47. http://dx.doi.org/10.1590/S0100-204X2012000900004 

    상세보기

26. Computers and Geoscience Y. Liu 59 98 2013 10.1016/j.cageo.2013.03.024 Liu, Y., Zhang, B., Wang, L-m., and Wang, N., 2013, A self-trained semi supervised SVM approach to the remote sensing land cover classification. Computers and Geoscience, 59, 98-107. 

    상세보기

27. IEEE Transactions on geoscience and remote sensing F. Melgani 42 1778 2004 10.1109/TGRS.2004.831865 Melgani, F. and Bruzzone, L., 2004, Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on geoscience and remote sensing, 42, 1778-1790. 

    상세보기

28. ISPRS Journal of Photogrammetry and Remote Sensing G. Mountrakis 66 247 2011 10.1016/j.isprsjprs.2010.11.001 Mountrakis, G., Im, J., and Ogole, C., 2011, Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259. 

    상세보기

29. Geological Society of America Bulletin J.D. Myers 100 1040 1988 10.1130/0016-7606(1988)100<1040:PPRBLA>2.3.CO;2 Myers, J.D., 1988, Possible petrogenetic relations between low- and high-MgO Aleutian basalts. Geological Society of America Bulletin, 100, 1040-1053. 

    

30. International Journal of Remote Sensing M. Pal 26 1007 2005 10.1080/01431160512331314083 Pal, M. and Mather, P., 2005, Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26, 1007-1011. 

    상세보기

31. Computers and Geoscience B. Pradhan 51 350 2013 10.1016/j.cageo.2012.08.023 Pradhan, B., 2013, A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers and Geoscience, 51, 350-365. 

    상세보기

32. J.A. Richards 494 1999 Remote Sensing Digital Image Analysis 10.1007/978-3-662-03978-6 Richards, J.A., 1999, Remote Sensing Digital Image Analysis. Springer, Berlin, 494 p. 

    

33. D.W. Scholl 123 1987 Geology and Resource Potential of the Continental Margin of Western North America and Adjacent Ocean Basins-Beaufort Sea to Baja California Scholl, D.W., Vallier, T.L., and Stevenson, A.J., 1987, Geologic evolution and petroleum geology of the Aleutian Ridge. In: Scholl, D.W., Grantz, A., and Vedder, J.G. (eds.), Geology and Resource Potential of the Continental Margin of Western North America and Adjacent Ocean Basins-Beaufort Sea to Baja California. Circum-Pacific Council for Energy and Mineral Resources, Houston, p. 123-155. 

34. ISPRS Journal of Photogrammetry and Remote Sensing Y. Shao 70 78 2012 10.1016/j.isprsjprs.2012.04.001 Shao, Y. and Lunetta, R.S., 2012, Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78-87. 

    상세보기

35. Journal of Hydrology M.S. Tehrany 512 332 2014 10.1016/j.jhydrol.2014.03.008 Tehrany, M.S., Pradhan, B., and Jebur, M.N., 2014, Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332-343. 

    상세보기

36. Journal of Mountain Science S. Ullah 13 1229 2016 10.1007/s11629-015-3456-3 Ullah, S., Farooq, M., Shafique, M., Siyab, M.A., Kareem, F., and Dees, M., 2016, Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan. Journal of Mountain Science, 13, 1229-1237. 

    상세보기

37. V. Vapnik 314 2000 The nature of statistical learning theory 10.1007/978-1-4757-3264-1 Vapnik, V., 2000, The nature of statistical learning theory. Springer-Verlag, New York, 314 p. 

    

38. Review of Palaeobotany and Palynology A.F. Weller 141 287 2006 10.1016/j.revpalbo.2006.06.001 Weller, A.F., Harris, A.J., and Ware, J.A., 2006, Artificial neural networks as potential classification tools for dinoflagellate cyst images: a case using the self-organizing map clustering algorithm. Review of Palaeobotany and Palynology, 141, 287-302. 

    상세보기

39. F.H. Wilson 196 2015 Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340 (scale 1:1,584,000) Wilson, F.H., Hults, C.P., Mull, C.G, and Karl, S.M., 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340 (scale 1:1,584,000). 2 sheets, Pamphlet, 196 p. http://dx.doi.org/10.3133/sim3340 

40. Remote Sensing Technology and Application L.N. Xiu 18 339 2003 Xiu, L.N. and Liu, X.G., 2003, Current status and future direction of the study on artificial neural network classification processing in remote sensing. Remote Sensing Technology and Application, 18, 339-345. 

    상세보기

41. X. Yongzhu 52 2010 International Conference on Computer Application and System Modeling (ICCASM) Yongzhu, X., Zhengdong, Z., and Feng, C., 2010, Comparison of artificial neural network and support vector machine methods for urban land use/cover classifications from remote sensing images a case study of Guangzhou, South China. International Conference on Computer Application and System Modeling (ICCASM), Taiyuan, Oct. 22-24, IEEE, 13, p. 52-56. 

42. Knowledge-Based System S. Zhai 65 50 2014 10.1016/j.knosys.2014.04.005 Zhai, S. and Jiang, T., 2014, A novel particle swarm optimization trained support vector machine for automatic sense-through-foliage target recognition system. Knowledge-Based System, 65, 50-59. 

    상세보기