In-depth comparisons of MaxEnt, biased SVM and one-class SVM for one-class classification of remote sensing data

References

• Amici, V. 2011. “Dealing with Vagueness in Complex Forest Landscapes: A Soft Classification Approach Through a Niche-Based Distribution Model.” Ecological Informatics 6 (6): 371–383. doi:10.1016/j.ecoinf.2011.07.001.
   Web of Science ®Google Scholar
• Anderson, R. P., and I. Gonzalez. 2011. “Species-Specific Tuning Increases Robustness to Sampling Bias in Models of Species Distributions: An Implementation with Maxent.” Ecological Modelling 222 (15): 2796–2811. doi:10.1016/j.ecolmodel.2011.04.011.
   Web of Science ®Google Scholar
• Baldeck, C. A., and G. P. Asner. 2015. “Single-Species Detection with Airborne Imaging Spectroscopy Data: A Comparison of Support Vector Techniques.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (6): 2501–2512. doi:10.1109/JSTARS.2014.2346475.
   Web of Science ®Google Scholar
• Blanchard, G., G. Lee, and C. Scott. 2010. “Semi-Supervised Novelty Detection.” Journal of Machine Learning Research 11, 2973–3009..
   Web of Science ®Google Scholar
• Chang, C.-C., and C.-J. Lin. 2015. “LIBSVM: A Library for Support Vector Machines.” https://www.csie.ntu.edu.tw/{-}cjlin/libsvm/.
   Google Scholar
• de Morsier, F., D. Tuia, M. Borgeaud, V. Gass, and J.-P. Thiran. 2013. “Semi-Supervised Novelty Detection Using SVM Entire Solution Path.” IEEE Transactions on Geoscience and Remote Sensing 51 (4): 1939–1950. doi:10.1109/TGRS.2012.2236683.
   Web of Science ®Google Scholar
• Elith, J., S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. J. Yates. 2011. “A Statistical Explanation of Maxent for Ecologists.” Diversity and Distributions 17 (1): 43–57. doi:10.1111/j.1472-4642.2010.00725.x.
   Web of Science ®Google Scholar
• Evangelista, P. H., T. J. Stohlgren, J. T. Morisette, and S. Kumar. 2009. “Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data.” Remote Sensing 1 (3): 519–533. doi:10.3390/rs1030519.
   Web of Science ®Google Scholar
• Fawcett, T. 2006. “An Introduction to ROC Analysis.” Pattern Recognition Letters 27 (8): 861–874. doi:10.1016/j.patrec.2005.10.010.
   Web of Science ®Google Scholar
• Foody, G. M., A. Mathur, C. Sanchez-Hernandez, and D. S. Boyd. 2006. “Training Set Size Requirements for the Classification of a Specific Class.” Remote Sensing of Environment 104 (1): 1–14. doi:10.1016/j.rse.2006.03.004.
   Web of Science ®Google Scholar
• Lee, W. S., and B. Liu. 2003. “Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression.” In Proceedings of the Twentieth International Conference on Machine Learning, edited by T. Fawcett and N. Mishra 448–455. Menlo Park, CA: The AAAI Press. http://www.aaai.org/Library/ICML/icml03contents.php
   Google Scholar
• Li, W., and Q. Guo. 2010. “A Maximum Entropy Approach to One-Class Classification of Remote Sensing Imagery.” International Journal of Remote Sensing 31 (8): 2227–2235. doi:10.1080/01431161003702245.
   Web of Science ®Google Scholar
• Li, W., and Q. Guo. 2014. “A New Accuracy Assessment Method for One-Class Remote Sensing Classification.” IEEE Transactions on Geoscience and Remote Sensing 52 (8): 4621–4632. doi:10.1109/TGRS.2013.2283082.
   Web of Science ®Google Scholar
• Li, W., Q. Guo, and C. Elkan. 2011. “A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data.” IEEE Transactions on Geoscience and Remote Sensing 49 (2): 717–725. doi:10.1109/TGRS.2010.2058578.
   Web of Science ®Google Scholar
• Lin, J. Y., X. P. Liu, K. Li, and X. Li. 2014. “A Maximum Entropy Method to Extract Urban Land by Combining MODIS Reflectance, MODIS NDVI, and DMSP-OLS Data.” International Journal of Remote Sensing 35 (18): 6708–6727. doi:10.1080/01431161.2014.960623.
   Web of Science ®Google Scholar
• Liu, B., Y. Dai, X. Li, W. L. Lee, and P. S. Yu. 2003. “Building Text Classifiers Using Positive and Unlabeled Examples.” Third IEEE International Conference on Data Mining, 179–186, Melbourne, Nov 19-22.
   Google Scholar
• Mack, B., R. Roscher, S. Stenzel, H. Feilhauer, S. Schmidtlein, and B. Waske. 2016. “Mapping Raised Bogs with an Iterative One-Class Classification Approach.” ISPRS Journal of Photogrammetry and Remote Sensing (forthcoming).
   Web of Science ®Google Scholar
• Mack, B., R. Roscher, and B. Waske. 2014. “Can I Trust My One-Class Classification?” Remote Sensing 6 (9): 8779–8802. doi:10.3390/rs6098779.
   Web of Science ®Google Scholar
• Mantero, P., G. Moser, and S. B. Serpico. 2005. “Partially Supervised Classification of Remote Sensing Images through SVM-Based Probability Density Estimation.” IEEE Transactions on Geoscience and Remote Sensing 43 (3): 559–570. doi:10.1109/TGRS.2004.842022.
   Web of Science ®Google Scholar
• Minter, T. A. 1975. “Single-Class Classification.” In Proceedings of Symposium on Machine Processing of Remotely Sensed Data, 2A–12–2A–15. West Lafayette, IN.
   Google Scholar
• Morán-Ordóñez, A., S. Suárez-Seoane, J. Elith, L. Calvo, and E. de Luis. 2012. “Satellite Surface Reflectance Improves Habitat Distribution Mapping: A Case Study on Heath and Shrub Formations in the Cantabrian Mountains (NW Spain).” Diversity and Distributions 18 (6): 588–602. doi:10.1111/j.1472-4642.2011.00855.x.
   Web of Science ®Google Scholar
• Munoz-Mari, J., F. Bovolo, L. Gomez-Chova, L. Bruzzone, and G. Camp-Valls. 2010. “Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data.” IEEE Transactions on Geoscience and Remote Sensing 48 (8): 3188–3197. doi:10.1109/TGRS.2010.2045764.
   Web of Science ®Google Scholar
• Ortiz, S., J. Breidenbach, and G. Kändle. 2013. “Early Detection of Bark Beetle Green Attack Using TerraSAR-X and Rapideye Data.” Remote Sensing 5 (4): 1912–1931. doi:10.3390/rs5041912.
   Web of Science ®Google Scholar
• Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. “Maximum Entropy Modeling of Species Geographic Distributions.” Ecological Modelling 190 (3–4): 231–259. doi:10.1016/j.ecolmodel.2005.03.026.
   Web of Science ®Google Scholar
• Phillips, S. J., M. Dudík, and R. E. Schapire. 2004. “A Maximum Entropy Approach to Species Distribution Modeling.” Twenty-First International Conference on Machine Learning - ICML ’04, 83, Banff, July 4-8. New York: ACM Press.
   Google Scholar
• Saatchi, S., W. Buermann, H. ter Steege, S. Mori, and T. B. Smith. 2008. “Modeling Distribution of Amazonian Tree Species and Diversity Using Remote Sensing Measurements.” Remote Sensing of Environment 112 (5): 2000–2017. doi:10.1016/j.rse.2008.01.008.
   Web of Science ®Google Scholar
• Schölkopf, B., J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. 2001. “Estimating the Support of a High-Dimensional Distribution.” Neural Computation 13 (7): 1443–1471. doi:10.1162/089976601750264965.
   PubMed Web of Science ®Google Scholar
• Shcheglovitova, M., and R. P. Anderson. 2013. “Estimating Optimal Complexity for Ecological Niche Models: A Jackknife Approach for Species with Small Sample Sizes.” Ecological Modelling 269: 9–17. doi:10.1016/j.ecolmodel.2013.08.011.
   Web of Science ®Google Scholar
• Stenzel, S., H. Feilhauer, B. Mack, A. Metz, and S. Schmidtlein. 2014. “Remote Sensing of Scattered Natura 2000 Habitats Using a One-Class Classifier.” International Journal of Applied Earth Observation and Geoinformation 33: 211–217. doi:10.1016/j.jag.2014.05.012.
   Web of Science ®Google Scholar
• Tax, D. M. J., and R. P. W. Duin. 2004. “Support Vector Data Description.” Machine Learning 54 (1): 45–66. doi:10.1023/B:MACH.0000008084.60811.49.
   Web of Science ®Google Scholar
• Warren, D. L., and S. N. Seifert. 2010. “Ecological Niche Modeling in Maxent: The Importance of Model Complexity and the Performance of Model Selection Criteria.” Ecological Applications 21 (2): 335–342. doi:10.1890/10-1171.1.
   Web of Science ®Google Scholar