메뉴 건너뛰기




Volumn 39, Issue 9, 2018, Pages 2784-2817

Implementation of machine-learning classification in remote sensing: An applied review

Author keywords

Image classification; Land cover classification; Landcover mapping; Machine learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; IMAGE CLASSIFICATION; MAPPING; NEAREST NEIGHBOR SEARCH; NEURAL NETWORKS; REMOTE SENSING;

EID: 85048716904     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2018.1433343     Document Type: Review
Times cited : (1212)

References (113)
  • 1
    • 84901651305 scopus 로고    scopus 로고
    • Land-Use/Cover Classification in a Heterogeneous Coastal Landscape Using RapidEye Imagery: Evaluating the Performance of Random Forest and Support Vector Machines Classifiers
    • Adam, E., O. Mutanga, J. Odindi, and E. M. Abdel-Rahman. 2014. “Land-Use/Cover Classification in a Heterogeneous Coastal Landscape Using RapidEye Imagery: Evaluating the Performance of Random Forest and Support Vector Machines Classifiers.” International Journal of Remote Sensing 35 (10): 3440–3458. doi:10.1080/01431161.2014.903435.
    • (2014) International Journal of Remote Sensing , vol.35 , Issue.10 , pp. 3440-3458
    • Adam, E.1    Mutanga, O.2    Odindi, J.3    Abdel-Rahman, E.M.4
  • 2
    • 0000581356 scopus 로고
    • An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
    • Altman, N. S. 1992. “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression.” The American Statistician 46 (3): 175–185. doi:10.1080/00031305.1992.10475879.
    • (1992) The American Statistician , vol.46 , Issue.3 , pp. 175-185
    • Altman, N.S.1
  • 3
    • 35348920168 scopus 로고    scopus 로고
    • Feature Selection and Classification of Hyperspectral; Images with Support Vector Machines
    • Archibald, R., and G. Fann. 2007. “Feature Selection and Classification of Hyperspectral; Images with Support Vector Machines.” IEEE Geoscience and Remote Sensing Letters 4 (4): 674–677. doi:10.1109/LGRS.2007.905116.
    • (2007) IEEE Geoscience and Remote Sensing Letters , vol.4 , Issue.4 , pp. 674-677
    • Archibald, R.1    Fann, G.2
  • 4
    • 0031105739 scopus 로고    scopus 로고
    • Introduction Neural Networks in Remote Sensing
    • Atkinson, P. M., and A. R. L. Tatnall. 1997. “Introduction Neural Networks in Remote Sensing.” International Journal of Remote Sensing 18 (4): 699–709. doi:10.1080/014311697218700.
    • (1997) International Journal of Remote Sensing , vol.18 , Issue.4 , pp. 699-709
    • Atkinson, P.M.1    Tatnall, A.R.L.2
  • 7
    • 33750798496 scopus 로고    scopus 로고
    • Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images
    • Bazi, Y., and F. Melgani. 2006. “Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 44 (11): 3374–3385. doi:10.1109/TGRS.2006.880628.
    • (2006) IEEE Transactions on Geoscience and Remote Sensing , vol.44 , Issue.11 , pp. 3374-3385
    • Bazi, Y.1    Melgani, F.2
  • 8
    • 84961834117 scopus 로고    scopus 로고
    • Random Forest in Remote Sensing: A Review of Applications and Future Directions
    • Belgiu, M., and L. Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114: 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
    • (2016) ISPRS Journal of Photogrammetry and Remote Sensing , vol.114 , pp. 24-31
    • Belgiu, M.1    Drăguţ, L.2
  • 9
    • 77957988489 scopus 로고    scopus 로고
    • Class Prediction for High-Dimensional Class-Imbalanced Data
    • Blagus, R., and L. Lusa. 2010. “Class Prediction for High-Dimensional Class-Imbalanced Data.” BMC Bioinformatics 11 (523): 1–17. doi:10.1186/1471-2105-11-523.
    • (2010) BMC Bioinformatics , vol.11 , Issue.523 , pp. 1-17
    • Blagus, R.1    Lusa, L.2
  • 10
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • Breiman, L. 2001. “Random Forests.” Machine Learning 54 (1): 5–32. doi:10.1023/A:1010933404324.
    • (2001) Machine Learning , vol.54 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 11
    • 33746606930 scopus 로고    scopus 로고
    • Robust Support Vector Regression for Biophysical Variable Estimation from Remotely Sensed Data
    • Camps-Valls, G., L. Bruzzone, J. L. Rojo-Álvarez, and F. Melgani. 2006. “Robust Support Vector Regression for Biophysical Variable Estimation from Remotely Sensed Data.” IEEE Geoscience and Remote Sensing Letters 3 (3): 339–343. doi:10.1109/LGRS.2006.871748.
    • (2006) IEEE Geoscience and Remote Sensing Letters , vol.3 , Issue.3 , pp. 339-343
    • Camps-Valls, G.1    Bruzzone, L.2    Rojo-Álvarez, J.L.3    Melgani, F.4
  • 12
    • 85014293109 scopus 로고    scopus 로고
    • Modification of the Random Forest Algorithm to Avoid Statistical Dependence Problems When Classifying Remote Sensing Imagery
    • Cánovas-García, F., F. Alonso-Sarría, F. Gomariz-Castillo, and F. Oñate-Valdivieso. 2017. “Modification of the Random Forest Algorithm to Avoid Statistical Dependence Problems When Classifying Remote Sensing Imagery.” Computers & Geosciences 103: 1–11. doi:10.1016/j.cageo.2017.02.012.
    • (2017) Computers & Geosciences , vol.103 , pp. 1-11
    • Cánovas-García, F.1    Alonso-Sarría, F.2    Gomariz-Castillo, F.3    Oñate-Valdivieso, F.4
  • 13
    • 0035273693 scopus 로고    scopus 로고
    • Enhanced Algorithm Performance for Land Cover Classification from Remotely Sensed Data Using Bagging and Boosting
    • Chan, J. C. W., C. Huang, and R. DeFries. 2001. “Enhanced Algorithm Performance for Land Cover Classification from Remotely Sensed Data Using Bagging and Boosting.” IEEE Transactions on Geoscience and Remote Sensing Communications 39 (3): 693–695. doi:10.1109/36.911126.
    • (2001) IEEE Transactions on Geoscience and Remote Sensing Communications , vol.39 , Issue.3 , pp. 693-695
    • Chan, J.C.W.1    Huang, C.2    Defries, R.3
  • 14
    • 43949125818 scopus 로고    scopus 로고
    • Evaluation of Random Forests and Adaboost Tree-Based Ensemble Classification and Spectral Band Selection for Ecotope Mapping Using Airborne Hyperspectral Imagery
    • Chan, J. C. W., and D. Paelinckx. 2008. “Evaluation of Random Forests and Adaboost Tree-Based Ensemble Classification and Spectral Band Selection for Ecotope Mapping Using Airborne Hyperspectral Imagery.” Remote Sensing of Environment 112: 2999–3011. doi:10.1016/j.rse.2008.02.011.
    • (2008) Remote Sensing of Environment , vol.112 , pp. 2999-3011
    • Chan, J.C.W.1    Paelinckx, D.2
  • 17
    • 34249753618 scopus 로고
    • Support-Vector Networks
    • Cortes, C., and V. Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20: 273–297. doi:10.1007/BF00994018.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 19
    • 84926662675 scopus 로고
    • Nearest Neighbor Pattern Classification
    • Cover, T. M., and P. E. Hart. 1967. “Nearest Neighbor Pattern Classification.” IEEE Transactions on Information Theory 13: 21–27. doi:10.1109/TIT.1967.1053964.
    • (1967) IEEE Transactions on Information Theory , vol.13 , pp. 21-27
    • Cover, T.M.1    Hart, P.E.2
  • 22
    • 84455200427 scopus 로고    scopus 로고
    • A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery
    • Duro, D. C., S. E. Franklin, and M. F. Dubé. 2012a. “A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery.” Remote Sensing of Environment 118: 259–272. doi:10.1016/j.rse.2011.11.020.
    • (2012) Remote Sensing of Environment , vol.118 , pp. 259-272
    • Duro, D.C.1    Franklin, S.E.2    Dubé, M.F.3
  • 23
    • 84856982478 scopus 로고    scopus 로고
    • Multi-Scale Object-Based Analysis and Feature Selection of Multi-Sensor Earth Observation Imagery Using Random Forests
    • Duro, D. S., S. E. Franklin, and M. F. Dubé. 2012b. “Multi-Scale Object-Based Analysis and Feature Selection of Multi-Sensor Earth Observation Imagery Using Random Forests.” International Journal of Remote Sensing 33 (14): 4502–4526. doi:10.1080/01431161.2011.649864.
    • (2012) International Journal of Remote Sensing , vol.33 , Issue.14 , pp. 4502-4526
    • Duro, D.S.1    Franklin, S.E.2    Dubé, M.F.3
  • 24
    • 0031105722 scopus 로고    scopus 로고
    • An Evaluation of Some Factors Affecting the Accuracy of Classification by an Artificial Neural Network
    • Foody, F. M., and M. K. Arora. 1997. “An Evaluation of Some Factors Affecting the Accuracy of Classification by an Artificial Neural Network.” International Journal of Remote Sensing 18: 799–810. doi:10.1080/014311697218764.
    • (1997) International Journal of Remote Sensing , vol.18 , pp. 799-810
    • Foody, F.M.1    Arora, M.K.2
  • 25
    • 3042654673 scopus 로고    scopus 로고
    • A Relative Evaluation of Multiclass Image Classification by Support Vector Machines
    • Foody, G. M., and H. Mathur. 2004. “A Relative Evaluation of Multiclass Image Classification by Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (6): 1335–1343. doi:10.1109/TGRS.2004.827257.
    • (2004) IEEE Transactions on Geoscience and Remote Sensing , vol.42 , Issue.6 , pp. 1335-1343
    • Foody, G.M.1    Mathur, H.2
  • 26
    • 84994156533 scopus 로고    scopus 로고
    • The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classification with Imperfect Reference Data
    • Foody, G. M., M. Pal., D. Rocchini, C. X. Garzon-Lopez, and L. Bastin. 2016. “The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classification with Imperfect Reference Data.” International Journal of Geo-Information 5 (11): 1–20. doi:10.3390/ijgi5110199.
    • (2016) International Journal of Geo-Information , vol.5 , Issue.11 , pp. 1-20
    • Foody, G.M.1    Pal, M.2    Rocchini, D.3    Garzon-Lopez, C.X.4    Bastin, L.5
  • 28
    • 0031211090 scopus 로고    scopus 로고
    • A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
    • Freund, Y., and R. E. Schapire. 1997. “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.” Journal of Computer and System Sciences 55: 119–139. doi:10.1006/jcss.1997.1504.
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 29
    • 0344972104 scopus 로고    scopus 로고
    • Decision Tree Classification of Land Cover Form Remotely Sensed Data
    • Friedl, M. A., and C. E. Brodley. 1997. “Decision Tree Classification of Land Cover Form Remotely Sensed Data.” Remote Sensing of Environment 61: 399–409. doi:10.1016/S0034-4257(97)00049-7.
    • (1997) Remote Sensing of Environment , vol.61 , pp. 399-409
    • Friedl, M.A.1    Brodley, C.E.2
  • 30
    • 62649107523 scopus 로고    scopus 로고
    • The Classification of Complex Data Sets: An Operational Comparison of Artificial Neural Networks and Decision Tree Classifiers
    • Gahegan, M., and G. West. 1998. “The Classification of Complex Data Sets: An Operational Comparison of Artificial Neural Networks and Decision Tree Classifiers.” Proceedings of the 3rd International Conference on GeoComputation 17–19. http://www.geocomputation.org/1998/61/gc_61.htm
    • (1998) Proceedings of the 3Rd International Conference on Geocomputation , pp. 17-19
    • Gahegan, M.1    West, G.2
  • 32
    • 84866847148 scopus 로고    scopus 로고
    • An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA
    • Ghimire, B., J. Rogan, V. Rodríguez-Galiano, P. Panday, and N. Neeti. 2012. “An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA.” GIScience & Remote Sensing 49 (5): 623–643. doi:10.2747/1548-1603.49.5.623.
    • (2012) Giscience & Remote Sensing , vol.49 , Issue.5 , pp. 623-643
    • Ghimire, B.1    Rogan, J.2    Rodríguez-Galiano, V.3    Panday, P.4    Neeti, N.5
  • 33
    • 84897585667 scopus 로고    scopus 로고
    • A Framework for Mapping Tree Species Combining Hyperspectral and LiDAR Data: Role of Selected Classifiers and Sensor across Three Spatial Scales
    • Ghosh, A., F. E. Fassnacht, P. K. Joshi, and B. Koch. 2014. “A Framework for Mapping Tree Species Combining Hyperspectral and LiDAR Data: Role of Selected Classifiers and Sensor across Three Spatial Scales.” International Journal of Applied Earth Observation and Geoinformation 26: 49–63. doi:10.1016/j.jag.2013.05.017.
    • (2014) International Journal of Applied Earth Observation and Geoinformation , vol.26 , pp. 49-63
    • Ghosh, A.1    Fassnacht, F.E.2    Joshi, P.K.3    Koch, B.4
  • 35
    • 78650731656 scopus 로고    scopus 로고
    • Relevance of Airborne LiDAR and Multispectral Image Data for Urban Scene Classification Using Random Forests
    • Guo, L., N. Chehata, C. Mallet, and S. Boukir. 2011. “Relevance of Airborne LiDAR and Multispectral Image Data for Urban Scene Classification Using Random Forests.” ISPRS Journal of Photogrammetry and Remote Sensing 66: 56–66. doi:10.1016/j.isprsjprs.2010.08.007.
    • (2011) ISPRS Journal of Photogrammetry and Remote Sensing , vol.66 , pp. 56-66
    • Guo, L.1    Chehata, N.2    Mallet, C.3    Boukir, S.4
  • 36
  • 38
    • 0029667616 scopus 로고    scopus 로고
    • Classification Trees: An Alternative to Traditional Land Cover Classifiers
    • Hansen, M., R. Dubayah, and R. DeFries. 1996. “Classification Trees: An Alternative to Traditional Land Cover Classifiers.” International Journal of Remote Sensing 17 (5): 1075–1081. doi:10.1080/01431169608949069.
    • (1996) International Journal of Remote Sensing , vol.17 , Issue.5 , pp. 1075-1081
    • Hansen, M.1    Dubayah, R.2    Defries, R.3
  • 39
    • 0034656178 scopus 로고    scopus 로고
    • A Comparison of the IGBP DISCover and University of Maryland 1 Km Global Land-Cover Products
    • Hansen, M. C., and B. Reed. 2000. “A Comparison of the IGBP DISCover and University of Maryland 1 Km Global Land-Cover Products.” International Journal of Remote Sensing 21 (6–7): 1365–1373. doi:10.1080/014311600210218.
    • (2000) International Journal of Remote Sensing , vol.21 , Issue.6-7 , pp. 1365-1373
    • Hansen, M.C.1    Reed, B.2
  • 40
    • 84896365008 scopus 로고    scopus 로고
    • High-Resolution Landcover Classification Using Random Forests
    • Hayes, M. M., S. N. Miller, and M. A. Murphy. 2014. “High-Resolution Landcover Classification Using Random Forests.” Remote Sensing Letters 5 (2): 112–121. doi:10.1080/2150704X.2014.882526.
    • (2014) Remote Sensing Letters , vol.5 , Issue.2 , pp. 112-121
    • Hayes, M.M.1    Miller, S.N.2    Murphy, M.A.3
  • 42
    • 85025688337 scopus 로고    scopus 로고
    • A Time Series of Annual Land Use and Land Cover Maps of China from 1982 to 2013 Generated Using AVHRR GIMMS NDVI3g Data
    • He, Y., E. Lee, and T. A. Warner. 2017. “A Time Series of Annual Land Use and Land Cover Maps of China from 1982 to 2013 Generated Using AVHRR GIMMS NDVI3g Data.” Remote Sensing of Environment 199: 201–217. doi:10.1016/j.rse.2017.07.010.
    • (2017) Remote Sensing of Environment , vol.199 , pp. 201-217
    • He, Y.1    Lee, E.2    Warner, T.A.3
  • 43
    • 4143112403 scopus 로고    scopus 로고
    • Development of a 2001 National Land-Cover Database for the United States
    • Homer, C., C. Huang, L. Yuang, B. Wylie, and M. Coan. 2004. “Development of a 2001 National Land-Cover Database for the United States.” Photogrammetric Engineering & Remote Sensing 70 (7): 829–840. doi:10.14358/PERS.70.7.829.
    • (2004) Photogrammetric Engineering & Remote Sensing , vol.70 , Issue.7 , pp. 829-840
    • Homer, C.1    Huang, C.2    Yuang, L.3    Wylie, B.4    Coan, M.5
  • 45
    • 0037138473 scopus 로고    scopus 로고
    • An Assessment of Support Vector Machines for Land Cover Classification
    • 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 (4): 725–749. doi:10.1080/01431160110040323.
    • (2002) International Journal of Remote Sensing , vol.23 , Issue.4 , pp. 725-749
    • Huang, C.1    Davis, L.S.2    Townshend, J.R.G.3
  • 46
    • 77957741951 scopus 로고
    • On the Man Accuracy of Statistical Pattern Recognizers
    • Hughes, G. F. 1968. “On the Man Accuracy of Statistical Pattern Recognizers.” IEEE Transactions on Information Theory 14: 55–63. doi:10.1109/TIT.1968.1054102.
    • (1968) IEEE Transactions on Information Theory , vol.14 , pp. 55-63
    • Hughes, G.F.1
  • 47
    • 84869054245 scopus 로고    scopus 로고
    • High Resolution Urban Land Cover Classification Using a Competitive Multi- Scale Object-Based Approach
    • Johnson, B. 2013. “High Resolution Urban Land Cover Classification Using a Competitive Multi- Scale Object-Based Approach.” Remote Sensing Letters 4 (2): 131–140. doi:10.1080/2150704X.2012.705440.
    • (2013) Remote Sensing Letters , vol.4 , Issue.2 , pp. 131-140
    • Johnson, B.1
  • 48
    • 84879561232 scopus 로고    scopus 로고
    • Classifying a High Resolution Image of an Urban Area Using Super- Object Information
    • Johnson, B., and Z. Xie. 2013. “Classifying a High Resolution Image of an Urban Area Using Super- Object Information.” ISPRS Journal of Photogrammetry and Remote Sensing 83: 40–49. doi:10.1016/j.isprsjprs.2013.05.008.
    • (2013) ISPRS Journal of Photogrammetry and Remote Sensing , vol.83 , pp. 40-49
    • Johnson, B.1    Xie, Z.2
  • 49
    • 85048703404 scopus 로고    scopus 로고
    • Kernlab: Kernel-Based Machine Learning Lab
    • Karatzoglou, A., A. Smola, and K. Hornik. 2016. “Kernlab: Kernel-Based Machine Learning Lab.” R package version 0.9-25. https://cran.r-project.org/web/packages/kernlab/index.html.
    • (2016) R Package Version 0.9-25
    • Karatzoglou, A.1    Smola, A.2    Hornik, K.3
  • 50
    • 67650759361 scopus 로고    scopus 로고
    • A Kernel Function Analysis for Support Vector Machines for Land Cover Classification
    • Kavzoglu, T., and I. Colkesen. 2009. “A Kernel Function Analysis for Support Vector Machines for Land Cover Classification.” International Journal of Applied Earth Observation and Geoinformation 11 (5): 352–359. doi:10.1016/j.jag.2009.06.002.
    • (2009) International Journal of Applied Earth Observation and Geoinformation , vol.11 , Issue.5 , pp. 352-359
    • Kavzoglu, T.1    Colkesen, I.2
  • 51
    • 0346245214 scopus 로고    scopus 로고
    • The Use of Backpropagation Artificial Neural Networks in Land Cover Classification
    • Kavzoglu, T., and P. M. Mather. 2003. “The Use of Backpropagation Artificial Neural Networks in Land Cover Classification.” International Journal of Remote Sensing 24: 4907–4938. doi:10.1080/0143116031000114851.
    • (2003) International Journal of Remote Sensing , vol.24 , pp. 4907-4938
    • Kavzoglu, T.1    Mather, P.M.2
  • 52
    • 84909594503 scopus 로고    scopus 로고
    • A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning
    • Khalid, S., T. Khalil, and S. Nasreen. 2014. “A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning.” 2014 Science and Information Conference 372–378. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6918213
    • (2014) 2014 Science and Information Conference , pp. 372-378
    • Khalid, S.1    Khalil, T.2    Nasreen, S.3
  • 55
    • 77958158373 scopus 로고    scopus 로고
    • Feature Selection with the Boruta Package
    • Kursa, M. B., and W. R. Rudnicki. 2010. “Feature Selection with the Boruta Package.” Journal of Statistical Software 36: 1–13. doi:10.18637/jss.v036.i11.
    • (2010) Journal of Statistical Software , vol.36 , pp. 1-13
    • Kursa, M.B.1    Rudnicki, W.R.2
  • 56
    • 84864510265 scopus 로고    scopus 로고
    • A Comparison of Three Feature Selection Methods for Object-Based Classification of Sub-Decimeter Resolution UltraCam-L Imagery
    • Laliberte, A. S., D. M. Browning, and A. Rango. 2012. “A Comparison of Three Feature Selection Methods for Object-Based Classification of Sub-Decimeter Resolution UltraCam-L Imagery.” International Journal of Applied Earth Observation and Geoinformation 15: 70–78. doi:10.1016/j.jag.2011.05.011.
    • (2012) International Journal of Applied Earth Observation and Geoinformation , vol.15 , pp. 70-78
    • Laliberte, A.S.1    Browning, D.M.2    Rango, A.3
  • 57
    • 84942543920 scopus 로고    scopus 로고
    • The AmericaView Classification Methods Accuracy Project: A Rigorous Approach for Model Selection
    • Lawrence, R. L., and C. J. Moran. 2015. “The AmericaView Classification Methods Accuracy Project: A Rigorous Approach for Model Selection.” Remote Sensing of Environment 170: 115–120. doi:10.1016/j.rse.2015.09.008.
    • (2015) Remote Sensing of Environment , vol.170 , pp. 115-120
    • Lawrence, R.L.1    Moran, C.J.2
  • 58
    • 31344453556 scopus 로고    scopus 로고
    • Mapping Invasive Plants Using Hyperspectral Imagery and Breiman Cutler Classifications (Random Forests)
    • Lawrence, R. L., S. D. Wood, and R. L. Sheley. 2006. “Mapping Invasive Plants Using Hyperspectral Imagery and Breiman Cutler Classifications (Random Forests).” Remote Sensing of Environment 100: 356–362. doi:10.1016/j.rse.2005.10.014.
    • (2006) Remote Sensing of Environment , vol.100 , pp. 356-362
    • Lawrence, R.L.1    Wood, S.D.2    Sheley, R.L.3
  • 59
    • 84894607481 scopus 로고    scopus 로고
    • Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
    • Li, C., J. Wang, L. Wang, L. Hu, and P. Gong. 2014. “Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery.” Remote Sensing 6 (2): 964–983. doi:10.3390/rs6020964.
    • (2014) Remote Sensing , vol.6 , Issue.2 , pp. 964-983
    • Li, C.1    Wang, J.2    Wang, L.3    Hu, L.4    Gong, P.5
  • 60
    • 55449099294 scopus 로고    scopus 로고
    • Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms
    • Lippitt, C. D., J. Rogan, Z. Li., J. R. Eastman, and T. G. Jones. 2008. “Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms.” Photogrammetric Engineering & Remote Sensing 74 (10): 1201–1211. doi:10.14358/PERS.74.10.1201.
    • (2008) Photogrammetric Engineering & Remote Sensing , vol.74 , Issue.10 , pp. 1201-1211
    • Lippitt, C.D.1    Rogan, J.2    Li, Z.3    Eastman, J.R.4    Jones, T.G.5
  • 62
    • 33947591833 scopus 로고    scopus 로고
    • A Survey of Image Classification Methods and Techniques for Improving Classification Performance
    • Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques for Improving Classification Performance.” International Journal of Remote Sensing 28 (5): 823–870. doi:10.1080/01431160600746456.
    • (2007) International Journal of Remote Sensing , vol.28 , Issue.5 , pp. 823-870
    • Lu, D.1    Weng, Q.2
  • 63
    • 39749162214 scopus 로고    scopus 로고
    • Object-Based Classification Using Quickbird Imagery for Delineating Forest Vegetation Polygons in a Mediterranean Test Site
    • Mallinis, G., N. Koutsias, M. Tsakiri-Strati, and M. Karteris. 2008. “Object-Based Classification Using Quickbird Imagery for Delineating Forest Vegetation Polygons in a Mediterranean Test Site.” ISPRS Journal of Photogrammetry and Remote Sensing 63 (2): 237–250. doi:10.1016/j.isprsjprs.2007.08.007.
    • (2008) ISPRS Journal of Photogrammetry and Remote Sensing , vol.63 , Issue.2 , pp. 237-250
    • Mallinis, G.1    Koutsias, N.2    Tsakiri-Strati, M.3    Karteris, M.4
  • 64
    • 26844554427 scopus 로고    scopus 로고
    • Estimation of Mediterranean Forest Attributes by the Application of k-NN Procedures to Multitemporal Landsat ETM+ Images
    • Maselli, F., G. Chirici, L. Bottai, P. Corona, and M. Marchetti. 2005. “Estimation of Mediterranean Forest Attributes by the Application of k-NN Procedures to Multitemporal Landsat ETM+ Images.” International Journal of Remote Sensing 26 (17): 3781–3796. doi:10.1080/01431160500166433.
    • (2005) International Journal of Remote Sensing , vol.26 , Issue.17 , pp. 3781-3796
    • Maselli, F.1    Chirici, G.2    Bottai, L.3    Corona, P.4    Marchetti, M.5
  • 67
    • 84901800835 scopus 로고    scopus 로고
    • Comparison of NAIP Orthophotography and RapidEye Satellite Imagery for Mapping of Mining and Mine Reclamation
    • Maxwell, A. E., M. P. Strager, T. A. Warner, N. P. Zégre, and C. B. Yuill. 2014a. “Comparison of NAIP Orthophotography and RapidEye Satellite Imagery for Mapping of Mining and Mine Reclamation.” GIScience & Remote Sensing 51 (3): 310–320. doi:10.1080/15481603.2014.912874.
    • (2014) Giscience & Remote Sensing , vol.51 , Issue.3 , pp. 310-320
    • Maxwell, A.E.1    Strager, M.P.2    Warner, T.A.3    Zégre, N.P.4    Yuill, C.B.5
  • 68
    • 84988310639 scopus 로고    scopus 로고
    • Predicting Palustrine Wetland Probabilities Using Random Forest Machine Learning and Digital Elevation Data-Derived Terrain Variables
    • Maxwell, A. E., T. A. Warner, and M. P. Strager. 2016. “Predicting Palustrine Wetland Probabilities Using Random Forest Machine Learning and Digital Elevation Data-Derived Terrain Variables.” Photogrammetric Engineering & Remote Sensing 82 (6): 437–447. doi:10.14358/PERS.82.6.437.
    • (2016) Photogrammetric Engineering & Remote Sensing , vol.82 , Issue.6 , pp. 437-447
    • Maxwell, A.E.1    Warner, T.A.2    Strager, M.P.3
  • 69
    • 84923357893 scopus 로고    scopus 로고
    • Assessing Machine- Learning Algorithms and Image- and LiDAR-derived Variables for GEOBIA Classification of Mining and Mine Reclamation
    • Maxwell, A. E., T. A. Warner, M. P. Strager, J. F. Conley, and A. L. Sharp. 2015. “Assessing Machine- Learning Algorithms and Image- and LiDAR-derived Variables for GEOBIA Classification of Mining and Mine Reclamation.” International Journal of Remote Sensing 36 (4): 954–978. doi:10.1080/01431161.2014.1001086.
    • (2015) International Journal of Remote Sensing , vol.36 , Issue.4 , pp. 954-978
    • Maxwell, A.E.1    Warner, T.A.2    Strager, M.P.3    Conley, J.F.4    Sharp, A.L.5
  • 70
    • 84893878381 scopus 로고    scopus 로고
    • Combining RapidEye Satellite Imagery and LiDAR for Mapping of Mining and Mine Reclamation
    • Maxwell, A. E., T. A. Warner, M. P. Strager, and M. Pal. 2014b. “Combining RapidEye Satellite Imagery and LiDAR for Mapping of Mining and Mine Reclamation.” Photogrammetric Engineering & Remote Sensing 80 (2): 179–189. doi:10.14358/PERS.80.2.179-189.
    • (2014) Photogrammetric Engineering & Remote Sensing , vol.80 , Issue.2 , pp. 179-189
    • Maxwell, A.E.1    Warner, T.A.2    Strager, M.P.3    Pal, M.4
  • 71
    • 4344614511 scopus 로고    scopus 로고
    • Classification of Hyperspectral Remote Sensing Images with Support Vector Machines
    • Melgani, F., and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (8): 1778–1790. doi:10.1109/TGRS.2004.831865.
    • (2004) IEEE Transactions on Geoscience and Remote Sensing , vol.42 , Issue.8 , pp. 1778-1790
    • Melgani, F.1    Bruzzone, L.2
  • 74
    • 74549225673 scopus 로고    scopus 로고
    • Quantifying Bufo Boreas Connectivity in Yellowstone National Park with Landscape Genetics
    • Murphy, M. A., J. S. Evans, and A. S. Storfer. 2010. “Quantifying Bufo Boreas Connectivity in Yellowstone National Park with Landscape Genetics.” Ecology 91: 252–261. doi:10.1890/08-0879.1.
    • (2010) Ecology , vol.91 , pp. 252-261
    • Murphy, M.A.1    Evans, J.S.2    Storfer, A.S.3
  • 75
    • 13344278660 scopus 로고    scopus 로고
    • Random Forest Classifier for Remote Sensing Classification
    • Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
    • (2005) International Journal of Remote Sensing , vol.26 , Issue.1 , pp. 217-222
    • Pal, M.1
  • 76
    • 33747119337 scopus 로고    scopus 로고
    • Support Vector Machine-Based Feature Selection for Land Cover Classification: A Case Study with DAIS Hyperspectral Data
    • Pal, M. 2006. “Support Vector Machine-Based Feature Selection for Land Cover Classification: A Case Study with DAIS Hyperspectral Data.” International Journal of Remote Sensing 27 (14): 2877–2894. doi:10.1080/01431160500242515.
    • (2006) International Journal of Remote Sensing , vol.27 , Issue.14 , pp. 2877-2894
    • Pal, M.1
  • 77
    • 43049106852 scopus 로고    scopus 로고
    • Ensemble of Support Vector Machines for Land Cover Classification
    • Pal, M. 2008. “Ensemble of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 29 (10): 3043–3049. doi:10.1080/01431160802007624.
    • (2008) International Journal of Remote Sensing , vol.29 , Issue.10 , pp. 3043-3049
    • Pal, M.1
  • 78
    • 77951295936 scopus 로고    scopus 로고
    • Feature Selection for Classification of Hyperspectral Data
    • Pal, M., and F. M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 48 (5): 2297–2307. doi:10.1109/TGRS.2009.2039484.
    • (2010) IEEE Transactions on Geoscience and Remote Sensing , vol.48 , Issue.5 , pp. 2297-2307
    • Pal, M.1    Foody, F.M.2
  • 79
    • 84869488312 scopus 로고    scopus 로고
    • Evaluation of SVM, RVM and SMLR for Accurate Image Classification with Limited Ground Data
    • Pal, M., and G. M. Foody. 2012. “Evaluation of SVM, RVM and SMLR for Accurate Image Classification with Limited Ground Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (5): 1344–1355. doi:10.1109/JSTARS.2012.2215310.
    • (2012) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol.5 , Issue.5 , pp. 1344-1355
    • Pal, M.1    Foody, G.M.2
  • 80
    • 0141569007 scopus 로고    scopus 로고
    • An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification
    • Pal, M., and P. M. Mather. 2003. “An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification.” Remote Sensing of Environment 86: 554–565. doi:10.1016/S0034-4257(03)00132-9.
    • (2003) Remote Sensing of Environment , vol.86 , pp. 554-565
    • Pal, M.1    Mather, P.M.2
  • 81
    • 13644256120 scopus 로고    scopus 로고
    • Support Vector Machines for Classification in Remote Sensing
    • Pal, M., and P. M. Mather. 2005. “Support Vector Machines for Classification in Remote Sensing.” International Journal of Remote Sensing 26 (5): 1007–1011. doi:10.1080/01431160512331314083.
    • (2005) International Journal of Remote Sensing , vol.26 , Issue.5 , pp. 1007-1011
    • Pal, M.1    Mather, P.M.2
  • 82
    • 84880397408 scopus 로고    scopus 로고
    • Kernel-Based Extreme Learning Machine for Remote-Sensing Image Classification
    • Pal, M., A. E. Maxwell, and T. A. Warner. 2013. “Kernel-Based Extreme Learning Machine for Remote-Sensing Image Classification.” Remote Sensing Letters 4 (9): 853–862. doi:10.1080/2150704X.2013.805279.
    • (2013) Remote Sensing Letters , vol.4 , Issue.9 , pp. 853-862
    • Pal, M.1    Maxwell, A.E.2    Warner, T.A.3
  • 83
    • 84857916067 scopus 로고    scopus 로고
    • Support Vector Machines and Object- Based Classification for Obtaining Land-Use/Cover Cartography Form Hyperion Hyperspectral Imagery
    • Petropoulos, G. P., C. Kalaizidis, and K. P. Vadrevu. 2012. “Support Vector Machines and Object- Based Classification for Obtaining Land-Use/Cover Cartography Form Hyperion Hyperspectral Imagery.” Computers & Geoscience 41 (2012): 99–107. doi:10.1016/j.cageo.2011.08.019.
    • (2012) Computers & Geoscience , vol.41 , Issue.2012 , pp. 99-107
    • Petropoulos, G.P.1    Kalaizidis, C.2    Vadrevu, K.P.3
  • 84
    • 33744584654 scopus 로고
    • Induction of Decision Trees
    • Quinlan, J. R. 1986. “Induction of Decision Trees.” Machine Learning 1: 81–106. doi:10.1007/BF00116251.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 85
    • 84961153928 scopus 로고    scopus 로고
    • R: A Language and Environment for Statistical Computing
    • R Core Development Team
    • R Core Development Team. 2016. “R: A Language and Environment for Statistical Computing.” R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/.
    • (2016) R Foundation for Statistical Computing. Vienna, Austria
  • 86
    • 84891793070 scopus 로고    scopus 로고
    • Nnet: Feed-Forward Neural Networks and Multinomial Log- Linear Models
    • Ripley, B., and W. Venables. 2016. “nnet: Feed-Forward Neural Networks and Multinomial Log- Linear Models.” R package version 7.3-12. https://cran.r-project.org/web/packages/nnet/index.html.
    • (2016) R Package Version , vol.7 , pp. 3-12
    • Ripley, B.1    Venables, W.2
  • 88
    • 41249103454 scopus 로고    scopus 로고
    • Mapping Land-Cover Modifications over Large Areas: A Comparison of Machine Learning Algorithms
    • Rogan, J., J. Franklin, D. Stow, J. Miller, C. Woodcock, and D. Roberts. 2008. “Mapping Land-Cover Modifications over Large Areas: A Comparison of Machine Learning Algorithms.” Remote Sensing of Environment 112 (5): 2272–2283. doi:10.1016/j.rse.2007.10.004.
    • (2008) Remote Sensing of Environment , vol.112 , Issue.5 , pp. 2272-2283
    • Rogan, J.1    Franklin, J.2    Stow, D.3    Miller, J.4    Woodcock, C.5    Roberts, D.6
  • 89
  • 91
    • 84865742501 scopus 로고    scopus 로고
    • Simultaneous Feature Selection and SVM Parameter Determination in Classification of Hyperspectral Imagery Using Ant Colony Optimization
    • Samadzadegan, F., H. Hasani, and T. Schenk. 2012. “Simultaneous Feature Selection and SVM Parameter Determination in Classification of Hyperspectral Imagery Using Ant Colony Optimization.” Canadian Journal of Remote Sensing 23 (2): 139–156. doi:10.5589/m12-022.
    • (2012) Canadian Journal of Remote Sensing , vol.23 , Issue.2 , pp. 139-156
    • Samadzadegan, F.1    Hasani, H.2    Schenk, T.3
  • 92
    • 84988416232 scopus 로고    scopus 로고
    • An Assessment of Algorithmic Parameters Affecting Image Classification Accuracy by Random Forests
    • Shi, D., and X. Yang. 2016. “An Assessment of Algorithmic Parameters Affecting Image Classification Accuracy by Random Forests.” Photogrammetric Engineering & Remote Sensing 82 (6): 407–417. doi:10.14358/PERS.82.6.407.
    • (2016) Photogrammetric Engineering & Remote Sensing , vol.82 , Issue.6 , pp. 407-417
    • Shi, D.1    Yang, X.2
  • 93
    • 84949812908 scopus 로고    scopus 로고
    • Accuracy Assessment
    • edited by T. A. Warner, M. D. Nellis, and G. M. Foody, London, UK: SAGE
    • Stehman, S. V., and G. M. Foody. 2009. “Accuracy Assessment.” In The SAGE Handbook of Remote Sensing, edited by T. A. Warner, M. D. Nellis, and G. M. Foody, 297–309.London, UK: SAGE.
    • (2009) The SAGE Handbook of Remote Sensing , pp. 297-309
    • Stehman, S.V.1    Foody, G.M.2
  • 94
  • 95
    • 77954817283 scopus 로고    scopus 로고
    • Party On! A New, Conditional Variable-Importance Measure for Random Forests Available in the Party Package
    • Strobl, C., T. Hothorn, and A. Zeileis. 2009. “Party On! A New, Conditional Variable-Importance Measure for Random Forests Available in the Party Package.” The R Journal 1-2: 14–17.
    • (2009) The R Journal , vol.1 , pp. 14-17
    • Strobl, C.1    Hothorn, T.2    Zeileis, A.3
  • 96
    • 79960743609 scopus 로고    scopus 로고
    • Object-Oriented Mapping of Landslides Using Random Forests
    • Stumpf, A., and N. Kerle. 2011. “Object-Oriented Mapping of Landslides Using Random Forests.” Remote Sensing of Environment 115: 2564–2577. doi:10.1016/j.rse.2011.05.013.
    • (2011) Remote Sensing of Environment , vol.115 , pp. 2564-2577
    • Stumpf, A.1    Kerle, N.2
  • 97
    • 67649669804 scopus 로고    scopus 로고
    • Optimizing Support Vector Machine Learning for Semi-Arid Vegetation Mapping by Using Clustering Analysis
    • Su, L. 2009. “Optimizing Support Vector Machine Learning for Semi-Arid Vegetation Mapping by Using Clustering Analysis.” IJRS Journal of Photogrammetry and Remote Sensing 64: 407–413. doi:10.1016/j.isprsjprs.2009.02.002.
    • (2009) IJRS Journal of Photogrammetry and Remote Sensing , vol.64 , pp. 407-413
    • Su, L.1
  • 98
    • 0018203420 scopus 로고
    • Fundamentals of Pattern Recognition in Remote Sensing
    • edited by P. H. Swain and S. M. Davis, New York: McGraw Hill
    • Swain, P. H. 1978. “Fundamentals of Pattern Recognition in Remote Sensing.” In Remote Sensing: The Quantitative Approach, edited by P. H. Swain and S. M. Davis, 136–187. New York: McGraw Hill.
    • (1978) Remote Sensing: The Quantitative Approach , pp. 136-187
    • Swain, P.H.1
  • 99
    • 84908354293 scopus 로고    scopus 로고
    • Rpart: Recursive Partitioning and Regression Trees
    • Therneau, T., B. Atkinson, and B. Ripley. 2017. “Rpart: Recursive Partitioning and Regression Trees.” R package version 4.1-11. https://cran.r-project.org/web/packages/rpart/index.html.
    • (2017) R Package Version , vol.4 , pp. 1-11
    • Therneau, T.1    Atkinson, B.2    Ripley, B.3
  • 100
    • 85048716408 scopus 로고    scopus 로고
    • Trimble, eCognition Developer 8.64.1 User Guide. Munich: Trimble
    • Trimble. 2011. eCognition Developer 8.64.1 User Guide. Munich: Trimble.
    • (2011)
  • 101
    • 79959689358 scopus 로고    scopus 로고
    • Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation
    • Tuia, D., J. Verrelst, L. Alonso, F. Pérez-Cruz, and G. Camps-Valls. 2011. “Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation.” IEEE Geoscience and Remote Sensing Letters 8 (4): 804–808. doi:10.1109/LGRS.2011.2109934.
    • (2011) IEEE Geoscience and Remote Sensing Letters , vol.8 , Issue.4 , pp. 804-808
    • Tuia, D.1    Verrelst, J.2    Alonso, L.3    Pérez-Cruz, F.4    Camps-Valls, G.5
  • 103
    • 82055178109 scopus 로고    scopus 로고
    • Applying Support Vector Regression to Water Quality Modelling by Remote Sensing
    • Wang, X., L. Fu, and C. He. 2011. “Applying Support Vector Regression to Water Quality Modelling by Remote Sensing.” International Journal of Remote Sensing 32 (23): 8615–8627. doi:10.1080/01431161.2010.543183.
    • (2011) International Journal of Remote Sensing , vol.32 , Issue.23 , pp. 8615-8627
    • Wang, X.1    Fu, L.2    He, C.3
  • 104
    • 70349329573 scopus 로고    scopus 로고
    • Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data
    • edited by J. A. Benediktsson, J. Kittler, and F. Roli, Berlin, Germany: Springer-Verlag
    • Waske, B., J. A. Benediktsson, and J. R. Sveinsson. 2009. “Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data.” In Multiple Classifier Systems, edited by J. A. Benediktsson, J. Kittler, and F. Roli, 375–384. Berlin, Germany: Springer-Verlag.
    • (2009) Multiple Classifier Systems , pp. 375-384
    • Waske, B.1    Benediktsson, J.A.2    Sveinsson, J.R.3
  • 105
    • 69849104695 scopus 로고    scopus 로고
    • Classifier Ensembles for Land Cover Mapping Using Multiemporal SAR Imagery
    • Waske, B., and M. Braun. 2009. “Classifier Ensembles for Land Cover Mapping Using Multiemporal SAR Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 64: 450–457. doi:10.1016/j.isprsjprs.2009.01.003.
    • (2009) ISPRS Journal of Photogrammetry and Remote Sensing , vol.64 , pp. 450-457
    • Waske, B.1    Braun, M.2
  • 106
    • 34047138873 scopus 로고    scopus 로고
    • Improved Wetland Remote Sensing in Yellowstone National Park Using Classification Trees to Combine TM Imagery and Ancillary Environmental Data
    • Wright, C., and A. Gallant. 2007. “Improved Wetland Remote Sensing in Yellowstone National Park Using Classification Trees to Combine TM Imagery and Ancillary Environmental Data.” Remote Sensing of Environment 107: 582–605. doi:10.1016/j.rse.2006.10.019.
    • (2007) Remote Sensing of Environment , vol.107 , pp. 582-605
    • Wright, C.1    Gallant, A.2
  • 107
    • 85048716891 scopus 로고    scopus 로고
    • Ranger: A Fast Implementation of Random Forests
    • Wright, M. N. 2017. “Ranger: A Fast Implementation of Random Forests.” R package version 0.8.0. https://cran.r-project.org/web/packages/ranger/index.html.
    • (2017) R Package Version 0.8
    • Wright, M.N.1
  • 108
    • 84904971625 scopus 로고    scopus 로고
    • Meta-Discoveries Form a Synthesis of Satellite-Based Land-Cover Mapping Research
    • Yu, L., L. Liang, J. Wang, Y. Zhao, Q. Cheng, L. Hu, S. Liu, et al. 2014. “Meta-Discoveries Form a Synthesis of Satellite-Based Land-Cover Mapping Research.” International Journal of Remote Sensing 35 (13): 4573–4588. doi:10.1080/01431161.2014.930206.
    • (2014) International Journal of Remote Sensing , vol.35 , Issue.13 , pp. 4573-4588
    • Yu, L.1    Liang, L.2    Wang, J.3    Zhao, Y.4    Cheng, Q.5    Hu, L.6    Liu, S.7
  • 109
    • 33745615125 scopus 로고    scopus 로고
    • Object-Based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery
    • Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer. 2006. “Object-Based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery.” Photogrammetric Engineering & Remote Sensing 72 (7): 799–811. doi:10.14358/PERS.72.7.799.
    • (2006) Photogrammetric Engineering & Remote Sensing , vol.72 , Issue.7 , pp. 799-811
    • Yu, Q.1    Gong, P.2    Clinton, N.3    Biging, G.4    Kelly, M.5    Schirokauer, D.6
  • 110
    • 84930423638 scopus 로고    scopus 로고
    • Spectral-Spatial Classification of Hyperspectral Images Using Deep Convolution Neural Networks
    • Yue, J., W. Zhao, S. Mao, and H. Liu. 2015. “Spectral-Spatial Classification of Hyperspectral Images Using Deep Convolution Neural Networks.” Remote Sensing Letters 6 (6): 468–477. doi:10.1080/2150704X.2015.1047045.
    • (2015) Remote Sensing Letters , vol.6 , Issue.6 , pp. 468-477
    • Yue, J.1    Zhao, W.2    Mao, S.3    Liu, H.4
  • 111
    • 84902481208 scopus 로고    scopus 로고
    • Object-Based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques
    • Zhang, C., and Z. Xie. 2013. “Object-Based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques.” Wetlands 33: 233–244. doi:10.1007/s13157-012-0373-x.
    • (2013) Wetlands , vol.33 , pp. 233-244
    • Zhang, C.1    Xie, Z.2
  • 112
    • 84976384382 scopus 로고    scopus 로고
    • Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
    • Zhang, L., L. Zhang, and B. Du. 2016. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 4 (2): 22–40. doi:10.1109/MGRS.2016.2540798.
    • (2016) IEEE Geoscience and Remote Sensing Magazine , vol.4 , Issue.2 , pp. 22-40
    • Zhang, L.1    Zhang, L.2    Du, B.3
  • 113
    • 70449384598 scopus 로고    scopus 로고
    • Feature Selection for Hyperspectral Data Based on Recursive Support Vector Machines
    • Zhang, R., and J. Ma. 2009. “Feature Selection for Hyperspectral Data Based on Recursive Support Vector Machines.” International Journal of Remote Sensing 30 (14): 3669–3677. doi:10.1080/01431160802609718.
    • (2009) International Journal of Remote Sensing , vol.30 , Issue.14 , pp. 3669-3677
    • Zhang, R.1    Ma, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.