A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

References

• Abedi, M., G. Norouzi, and A. Bahroudi. 2012. “Support Vector Machine for Multi-Classification of Mineral Prospectivity Areas.” Computers & Geosciences 46: 272–283. doi:10.1016/j.cageo.2011.12.014.
   Web of Science ®Google Scholar
• Akar, Ö., and O. Güngör. 2012. “Classification of Multispectral Images Using Random Forest Algorithm.” Journal of Geodesy and Geoinformation 1 (2): 105–112. doi:10.9733/jgg.241212.1.
   Google Scholar
• Arora, M. K., K. C. Tiwari, and B. Mohanty. 2000. “Effect of Neural Network Variables on Image Classification.” Asian Pacific Remote Sensing and GIS Journal 13: 1–11.
   Google Scholar
• Behnia, P., J. R. Harris, R. H. Rainbird, M. C. Williamson, and M. Sheshpari. 2012. “Remote Predictive Mapping of Bedrock Geology Using Image Classification of Landsat and SPOT Data, Western Minto Inlier, Victoria Island, Northwest Territories, Canada.” International Journal of Remote Sensing 33 (21): 6876–6903. doi:10.1080/01431161.2012.693219.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Monterey, CA: Wadsworth.
   Google Scholar
• Chen, D. 2008. “A Standardized Probability Comparison Approach for Evaluating and Combining Pixel-Based Classification Procedures.” Photogrammetric Engineering & Remote Sensing 74 (5): 601–609. doi:10.14358/PERS.74.5.601.
   Web of Science ®Google Scholar
• Chen, X., T. A. Warner, and D. J. Campagna. 2007. “Integrating Visible, Near-Infrared and Short-Wave Infrared Hyperspectral and Multispectral Thermal Imagery for Geological Mapping at Cuprite, Nevada.” Remote Sensing of Environment 110 (3): 344–356. doi:10.1016/j.rse.2007.03.015.
   Web of Science ®Google Scholar
• Congalton, R. G., and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, FL: CRC Press.
   Google Scholar
• Cracknell, M. J., and A. M. Reading. 2014. “Geological Mapping Using Remote Sensing Data: A Comparison of Five Machine Learning Algorithms, Their Response to Variations in the Spatial Distribution of Training Data and the Use of Explicit Spatial Information.” Computers & Geosciences 63: 22–33. doi:10.1016/j.cageo.2013.10.008.
   Web of Science ®Google Scholar
• Demšar, J. 2006. “Statistical Comparisons of Classifiers over Multiple Data Sets.” Journal of Machine Learning Research 7: 1–30.
   Web of Science ®Google Scholar
• El Janati, M., A. Soulaimani, H. Admou, N. Youbi, A. Hafid, and K. P. Hefferan. 2014. “Application of ASTER Remote Sensing Data to Geological Mapping of Basement Domains in Arid Regions: A Case Study from the Central Anti-Atlas, Iguerda Inlier, Morocco.” Arabian Journal of Geosciences 7 (6): 2407–2422. doi:10.1007/s12517-013-0945-y.
   Web of Science ®Google Scholar
• Fauvel, M., J. Chanussot, and J. A. Benediktsson. 2006. Evaluation of Kernels for Multiclass Classification of Hyperspectral Remote Sensing Data. In ICASSP 2006, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, May 14–19, 813–816. New York: IEEE.
   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: 179–189. doi:10.1016/j.rse.2006.04.001.
   Web of Science ®Google Scholar
• Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27: 294–300. doi:10.1016/j.patrec.2005.08.011.
   Web of Science ®Google Scholar
• Gupta, R. P. 1991. Remote Sensing Geology. Berlin: Springer-Verlag.
   Google Scholar
• Hadigheh, S. M. H., and H. Ranjbar. 2013. “Lithological Mapping in the Eastern Part of the Central Iranian Volcanic Belt Using Combined ASTER and IRS data.” Journal of the Indian Society of Remote Sensing 41 (4): 921–931. doi:10.1007/s12524-013-0284-1.
   Web of Science ®Google Scholar
• Harris, J. R., E. Grunsky, J. He, D. Gorodetzky, and N. Brown. 2012. “A Robust, Cross-Validation Classification Method (RCM) for Improved Mapping Accuracy and Confidence Metrics.” Canadian Journal of Remote Sensing 38 (1): 69–90. doi:10.5589/m12-013.
   Web of Science ®Google Scholar
• Harris, J. R., J. He, R. Rainbird, and P. Behnia. 2014. “A Comparison of Different Remotely Sensed Data for Classification Lithology in Canada’s Arctic: Application of the Robust Classification Method and Random Forests.” Geoscience Canada 41 (4): 557–584.
   Web of Science ®Google Scholar
• Harris, J. R., L. Wickert, T. Lynds, P. Behnia, R. Rainbird, E. Grunsky, R. McGregor, and E. Schetselaar. 2011. “Remote Predictive Mapping 3. Optical Remote Sensing—A Review for Remote Predictive Geological Mapping in Northern.” Geoscience Canada 38 (2): 49–83.
   Web of Science ®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 (4): 725–749. doi:10.1080/01431160110040323.
   Web of Science ®Google Scholar
• Kavzoglu, T., and I. Colkesen. 2009. “A Kernel Functions Analysis for Support Vector Machines for Land Cover Classification.” International Journal of Applied Earth Observation and Geoinformation 11: 352–359. doi:10.1016/j.jag.2009.06.002.
   Web of Science ®Google Scholar
• LaRocque, A., B. Leblon, J. Harris, C. Jefferson, V. Tschirhart, and Y. Shelat. 2012. “Surficial Materials Mapping in Nunavut, Canada with Multibeam RADARSAT-2 Dual-Polarization C-HH and C-HV, LANDSAT-7 ETM+, and DEM Data.” Canadian Journal of Remote Sensing 38 (3): 281–305. doi:10.5589/m12-020.
   Web of Science ®Google Scholar
• Leverington, D. W. 2010. “Discrimination of Sedimentary Lithologies Using Hyperion and Landsat Thematic Mapper Data: A Case Study at Melville Island, Canadian High Arctic.” International Journal of Remote Sensing 31 (1): 233–260. doi:10.1080/01431160902882637.
   Web of Science ®Google Scholar
• Leverington, D. W., and W. M. Moon. 2012. “Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada.” Remote Sensing 4 (5): 1208–1231. doi:10.3390/rs4051208.
   Web of Science ®Google Scholar
• Li, N., M. Frei, and W. Altermann. 2011. “Textural and Knowledge-Based Lithological Classification of Remote Sensing Data in Southwestern Prieska Sub-Basin, Transvaal Supergroup, South Africa.” Journal of African Earth Sciences 60 (4): 237–246. doi:10.1016/j.jafrearsci.2011.03.002.
   Web of Science ®Google Scholar
• Loizzo, R., L. G. Sylos, M. Pappalepore, P. Pieri, G. Pasquariello, and M. Antoninetti. 1995. “Multitemporal and Multisensor Signatures Evaluation for Lithologic Classification.” International Geoscience and Remote Sensing Symposium (IGARSS) 3: 2209–2211.
   Google Scholar
• Mas, J. F., and J. J. Flores. 2008. “The Application of Artificial Neural Networks to the Analysis of Remotely Sensed Data.” International Journal of Remote Sensing 29 (3): 617–663. doi:10.1080/01431160701352154.
   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
• Mountrakis, G., J. Im, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66: 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
   Web of Science ®Google Scholar
• Nitze, I., U. Schulthess, and H. Asche. 2012. “Comparison of Machine Learning Algorithms Random Forest, Artificial Neural Network and Support Vector Machine to Maximum Likelihood for Supervised Crop Type Classification.” In Proceeding of the 4th International Conference on Geographic Object Based Image Analysis(GEOBIA), Rio de Janeiro, May 7–9.
   Google Scholar
• Otukei, J. R., and T. Blaschke. 2010. “Land Cover Change Assessment Using Decision Trees, Support Vector Machines and Maximum Likelihood Classification Algorithms.” International Journal of Applied Earth Observation and Geoinformation 12: S27–S31. doi:10.1016/j.jag.2009.11.002.
   Web of Science ®Google Scholar
• Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Paola, J. D., and R. A. Schowengerdt. 1995. “A Review and Analysis of Backpropagation Neural Networks for Classification of Remotely-Sensed Multi-Spectral Imagery.” International Journal of Remote Sensing 16 (16): 3033–3058. doi:10.1080/01431169508954607.
   Web of Science ®Google Scholar
• Patel, N., and K. K. Rampal. 1993. “Lithological Discrimination of Sun-Facing and Shadowed Portions of Aravalli Region.” Advances in Space Research 13 (11): 91–94. doi:10.1016/0273-1177(93)90207-R.
   Web of Science ®Google Scholar
• Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. San Matero, CA: Morgan Kaufmann.
   Google Scholar
• Rabe, A., B. Jakimow, M. Held, S. van der Linden, and P. Hostert. 2014. EnMAP-Box, Version 2.0, software available at www.enmap.org
   Google Scholar
• Rainbird, R. H., J. H. Bédard, K. Dewing, T. Hadlari, and D. Thomson. 2013. Geology, Qiqittiivik, Victoria Island, Northwest Territories; Geological Survey of Canada, Canadian Geoscience Map 59, (ed. prelim.); 1 sheet.
   Google Scholar
• Rainbird, R. H., J. H. Bédard, and N. Williamson. 2013. Geology, Takiyuaqattak, Victoria Island, Northwest Territories; Geological Survey of Canada, Canadian Geoscience Map 104, (ed. prelim.); 1 sheet.
   Google Scholar
• Rainbird, R. H., R. L. Christie, J. C. Harrison, and A. Ford. 2013. Geology, Tectonic Assemblage Map of the Ulukhaktok Area, Southwestern Victoria Island, Nunavut - Northwest Territories; Geological Survey of Canada, Canadian Geoscience Map 77, (ed. prelim.); 1 sheet.
   Google Scholar
• Rainbird, R. H., J. C. Harrison, E. M. Hillary, A. Ford, L. J. Hulbert, R. L. Christie, and F. H. A. Campbell. 2013. Geology, Tectonic Assemblage Map of Hadley Bay, Victoria and Prince of Wales Islands, Nunavut - Northwest Territories; Geological Survey of Canada, Canadian Geoscience Map 75, (ed. prelim.); 1 sheet.
   Google Scholar
• Richard, J. A., and X. Jia. 2006. Remote Sensing Digital Image Analysis: An Introduction. Berlin, Heidelberg: Spring-Verlag.
   Google Scholar
• Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
   Web of Science ®Google Scholar
• Stephens, M. A. 1974. “EDF Statistics for Goodness of Fit and Some Comparisons.” Journal of the American Statistical Association 69: 730–737. doi:10.1080/01621459.1974.10480196.
   Web of Science ®Google Scholar
• Thorsteinsson, R., and E. T. Tozer. 1962. Banks, Victoria, and Stefansson islands, Arctic Archipelago. Ottawa, ON: Geological Survey of Canada. Memoir 330.
   Google Scholar
• Wang, X., R. Niu, and K. Wu. 2011. “Lithology Intelligent Identification Using Support Vector Machine and Adaptive Cellular Automata in Multispectral Remote Sensing Image.” Optical Engineering 50 (7): 076201–076213. doi:10.1117/1.3598315.
   Web of Science ®Google Scholar
• Waske, B., J. A. Benediktsson, K. Árnason, and J. R. Sveinsson. 2009. “Mapping of Hyperspectral AVIRIS Data Using Machine-Learning Algorithms.” Canadian Journal of Remote Sensing 35(Suppl. 1): SS106-116. doi:10.5589/m09-018.
   Web of Science ®Google Scholar
• Waske, B., S. van der Linden, J. A. Benediktsson, A. Rabe, and P. Hostert. 2010. “Sensitivity of Support Vector Machines to Random Feature Selection in classification of Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 48 (7): 2880–2889. doi:10.1109/TGRS.2010.2041784.
   Web of Science ®Google Scholar
• Yu, L., A. Porwal, E. Holden, and M. Dentith. 2012. “Towards Automatic Lithological Classification from Remote Sensing Data Using Support Vector Machines.” Computers & Geosciences 45: 229–239. doi:10.1016/j.cageo.2011.11.019.
   Web of Science ®Google Scholar