Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

References

• Arranz, J. J. 2013. “Diseño, Optimización Y Análisis de Sistemas Basados En Técnicas Láser, Para El Modelado Geométrico, Registro Y Documentación, Aplicados a Entidades de Interés Patrimonial.” PhD thesis, Technical University of Madrid, Madrid.
   Google Scholar
• Benito, B., D. Belizaire, Y. Torres Fernández, J. J. Martínez Díaz, V. Huérfano, E. Polanco, R. Garcia, P. González-Crende, A. R. Serna Martínez, and F. Zevallos. 2012. “An Evaluation of Seismic Hazard in La Hispaniola, after the 2010 Haiti Earthquake.” In XXXIII General Assembly European Seismological Commission. Moscow: European Seismological Commission. Accessed November 11, 2015. http://oa.upm.es/21850/
   Google Scholar
• Calais, E., A. Freed, G. Mattioli, F. Amelung, S. Jónsson, P. Jansma, S. Hong, T. Dixon, C. Prépetit, and R. Momplaisir. 2010. “Transpressional Rupture of an Unmapped Fault during the 2010 Haiti Earthquake.” Nature Geoscience 3 (11): 794–799. doi:10.1038/ngeo992.
   Web of Science ®Google Scholar
• Chang, C., and C. Lin. 2011. “LIBSVM: A Library for Support Vector Machines.” ACM Transactions on Intelligent Systems and Technology (TIST) 2 (3): 27.
   Web of Science ®Google Scholar
• Dey, V., Y. Zhang, and M. Zhong. 2010. “A Review on Image Segmentation Techniques with Remote Sensing Perspective.” In ISPRS TC VII Symposium – 100 Years ISPRS, edited by W. Wagner and B. Székely. Vienna: Institute of Photogrammetry and Remote Sensing, Vienna University of Technology.
   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
• Felzenszwalb, P., and D. Huttenlocher. 2004. “Efficient Graph-Based Image Segmentation.” International Journal of Computer Vision 59 (2): 167–181. doi:10.1023/B:VISI.0000022288.19776.77.
   Web of Science ®Google Scholar
• Foody, G. M., and A. Mathur. 2004. “Toward Intelligent Training of Supervised Image Classifications: Directing Training Data Acquisition for SVM Classification.” Remote Sensing of Environment 93 (1–2): 107–117. doi:10.1016/j.rse.2004.06.017.
   Web of Science ®Google Scholar
• Frankel, A., S. Harmsen, C. Mueller, E. Calais, and J. Haase. 2011. “Seismic Hazard Maps for Haiti.” Earthquake Spectra 27 (S1): S23–S41. doi:10.1193/1.3631016.
   Google Scholar
• Griffiths, P., P. Hostert, O. Gruebner, and S. van der Linden. 2010. “Mapping Megacity Growth with Multi-Sensor Data.” Remote Sensing of Environment 114 (2): 426–439. doi:10.1016/j.rse.2009.09.012.
   Web of Science ®Google Scholar
• Guyon, I., J. Weston, S. Barnhill, and V. Vapnik. 2002. “Gene Selection for Cancer Classification Using Support Vector Machines.” Machine Learning 46 (1/3): 389–422. doi:10.1023/A:1012487302797.
   Web of Science ®Google Scholar
• Haralick, R., K. Shanmugam, and I. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man, and Cybernetics 3 (6): 610–621. doi:10.1109/TSMC.1973.4309314.
   Web of Science ®Google Scholar
• Herold, M., X. Liu, and K. C. Clarke. 2003. “Spatial Metrics and Image Texture for Mapping Urban Land Use.” Photogrammetric Engineering & Remote Sensing 69 (9): 991–1001. doi:10.14358/PERS.69.9.991.
   Web of Science ®Google Scholar
• Huberty, C. J. 1994. Applied Discriminant Analysis. New York: Wiley.
   Google Scholar
• Hughes, G. 1968. “On the Mean Accuracy of Statistical Pattern Recognizers.” IEEE Transactions on Information Theory 14 (1): 55–63. doi:10.1109/TIT.1968.1054102.
   Web of Science ®Google Scholar
• Johnson, B., and Z. Xie. 2011. “Unsupervised Image Segmentation Evaluation and Refinement Using a Multi-Scale Approach.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (4): 473–483. doi:10.1016/j.isprsjprs.2011.02.006.
   Web of Science ®Google Scholar
• Kononenko, I. 1994. “Estimating Attributes: Analysis and Extensions of Relief.” Lecture Notes in Computer Science 784: 171–182.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Löw, F., U. Michel, S. Dech, and C. Conrad. 2013. “Impact of Feature Selection on the Accuracy and Spatial Uncertainty of per-Field Crop Classification Using Support Vector Machines.” ISPRS Journal of Photogrammetry and Remote Sensing 85: 102–119. doi:10.1016/j.isprsjprs.2013.08.007.
   Web of Science ®Google Scholar
• McGarigal, K., S. Cushman, and E. Banzhaf. 2012. FRAGSTATS: Spatial Pattern Analysis Program for Categorical and Continuous Maps (version 4). http://www.umass.edu/landeco/research/fragstats/fragstats.html
   Google Scholar
• “Open Topography.” 2015. Accessed December 7. http://www.opentopography.org/index.php/blog/detail/haiti_lidar_imagery_in_google_earth
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Peña, J., P. Gutiérrez, C. Hervás-Martínez, J. Six, R. Plant, and F. López-Granados. 2014. “Object-Based Image Classification of Summer Crops with Machine Learning Methods.” Remote Sensing 6 (6): 5019–5041. doi:10.3390/rs6065019.
   Web of Science ®Google Scholar
• Pittore, M., and M. Wieland. 2013. “Toward a Rapid Probabilistic Seismic Vulnerability Assessment Using Satellite and Ground-Based Remote Sensing.” Natural Hazards 68 (1): 115–145. doi:10.1007/s11069-012-0475-z.
   Web of Science ®Google Scholar
• “Plan de Prévention Séisme Pour Le Grand Nord.” 2015. Accessed December 6. http://www.ht.undp.org/content/haiti/fr/home/operations/projects/crisis_prevention_and_recovery/plan-de-prevention-seisme-pour-le-grand-nord-dhaiti.html
   Google Scholar
• “Population of Haiti.” 2015. Accessed December 6. http://www.citypopulation.de/Haiti.html
   Google Scholar
• Qian, Y., W. Zhou, J. Yan, W. Li, and L. Han. 2014. “Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery.” Remote Sensing 7 (1): 153–168. doi:10.3390/rs70100153.
   Web of Science ®Google Scholar
• Rousseeuw, P. J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics 20: 53–65. doi:10.1016/0377-0427(87)90125-7.
   Web of Science ®Google Scholar
• Schneider, A. 2012. “Monitoring Land Cover Change in Urban and Peri-Urban Areas Using Dense Time Stacks of Landsat Satellite Data and a Data Mining Approach.” Remote Sensing of Environment 124: 689–704. doi:10.1016/j.rse.2012.06.006.
   Web of Science ®Google Scholar
• “Scikit-Learn.” 2015. Accessed January 8. http://scikit-learn.org
   Google Scholar
• Song, C., C. E. Woodcock, K. C. Seto, M. P. Lenney, and S. A. Macomber. 2001. “Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?” Remote Sensing of Environment 75 (2): 230–244. doi:10.1016/S0034-4257(00)00169-3.
   Web of Science ®Google Scholar
• Tobler, W. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46 (2): 234–240. doi:10.2307/143141.
   Web of Science ®Google Scholar
• Torres, Y., S. Molina, S. Martínez-Cuevas, M. Navarro, J. Martínez-Díaz, B. Benito, J. Galiana, and D. Belizaire. 2016. “A First Approach to the Earthquake Loss Estimation in Haiti. Advises to Minimize the Seismic Risk.” Bulletin of Earthquake Engineering 14 (1): 39–58. doi:10.1007/s10518-015-9813-3.
   Web of Science ®Google Scholar
• “USGS Earthexplorer.” 2015. Accessed January 5. http://earthexplorer.usgs.gov/
   Google Scholar
• Vapnik, V. 2000. The Nature of Statistical Learning Theory. New York: Springer.
   Google Scholar
• Weiss, G. M., and F. Provost. 2003. “Learning When Training Data Are Costly: The Effect of Class Distribution on Tree Induction.” Journal of Artificial Intelligence Research 19: 315–354.
   Web of Science ®Google Scholar
• “WEKA3 Data Mining with Open Source Machine Learning Software.” 2012. Accessed October 29. http://www.cs.waikato.ac.nz/ml/weka/
   Google Scholar
• Wieland, M., and M. Pittore. 2014. “Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images.” Remote Sensing 6 (4): 2912–2939. doi:10.3390/rs6042912.
   Web of Science ®Google Scholar
• Wieland, M., M. Pittore, S. Parolai, U. Begaliev, P. Yasunov, J. Niyazov, S. Tyagunov, et al. 2015. “Towards a Cross-Border Exposure Model for the Earthquake Model Central Asia.” Annals of Geophysics 58 (1): S0106. doi:10.4401/ag-6663.
   Google Scholar