Segmentación y clasificación de imágenes SAR en zonas de inundación en Colombia, una herramienta computacional para prevención de desastres
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SAR, Clasificación, Segmentación, imágenes de zonas de inundaciónResumen
La detección de zonas de inundación es fundamental para la prevención de desastres, por este motivo en este trabajo se presenta una herramienta computacional desarrollada en MATLAB que ofrece una alternativa a las existentes en el mercado para la clasificación supervisada de imágenes SAR (Synthetic Aperture Radar) de zonas de inundación. En particular se usaron diferentes métodos de clasificación para seleccionar de acuerdo al desempeño el mejor para el estudio de zonas de inundación en Colombia.Los datos de entrenamiento fueron generados con los resultados de las segmentaciones Fuzzy-Clustering, K-means y Region-Growing sobre imágenes SAR de zonas de inundación. Los métodos de clasificación implementados fueron un clasificador basado en el método Bayesiano y un clasificador basado en máquinas de vectores de soporte (SVM). Para evaluar el desempeño de los clasificadores se utilizaron índices como la exactitud total, la exactitud dependiendo del usuario, el índice Kappay R’. De acuerdo a los resultados el clasificador basado en máquinas de soporte presenta mayor exactitud; sin embargo, el clasificador bayesiano se desempeña mejor clasificando pixeles que corresponden a poblaciones, aun con pocos datos de entrenamiento.
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M. J. Gambini, “Modelos de segmentación basados en regiones y contornos activos aplicados a imagenes de radar de apertura sintetica,” Ph.D. dissertation. Universidad de Buenos Aires Facultad de Ciencias Exactas y Naturales Departamento de Computacion, 2006.
Q. Yu and D. Clausi, “Sar sea-ice image analysis based on iterative region growing using semantics,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 45, no. 12, pp. 3919–3931, Dec 2007.
H.-M. Luo, E. Chen, X. Li, J. Cheng, and M. Li, “Unsupervised classi-fication of forest from polarimetric interferometric sar data using fuzzy clustering,” in Wavelet Analysis and Pattern Recognition (ICWAPR), 2010 International Conference on, 2010, pp. 201–206.
D. Samanta and G. Sanyal, “Segmentation technique of sar imagery based on fuzzy c-means clustering,” in Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on, 2012, pp. 610–612.
P. Yu, A. K. Qin, and D. Clausi, “Unsupervised polarimetric sar image segmentation and classification using region growing with edge penalty,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 50, no. 4, pp. 1302–1317, 2012.
A. Saepuloh, K. Koike, and M. Omura, “Applying bayesian decision classification to pi-sar polarimetric data for detailed extraction of the geomorphologic and structural features of an active volcano,” Geoscience and Remote Sensing Letters, IEEE, vol. 9, no. 4, pp. 554–558, 2012.
M. Pal and P. Mather, “Support vector machines for classification in remote sensing,” International Journal of Remote Sensing, pp. 1007– 1011, 2005.
C. Tan, J. Koay, K. Lim, H. Ewe, and H. Chuah, “Calssification of multi-temporal sar images for rice crops using combined entropy decomposition and support vector machine technique,” Progress in Electromagnetics Research, pp. 19–39, 2007.
C. Lardeux, P. Frison, C. Tison, J. Souyris, B. Stoll, and B. Fruneau, “Support vector machine for multifrequency sar polarimetric data classification,” IEEE Transactions on Geoscience and Remote Sensing, pp. 4143–4152, 2009.
C. Mladinich, “An evaluation of object-oriented image analysis techniques to identify motorized vehicle effects in semiarid to arid ecosystems of the american west,” GIScience & Remote Sensing, pp. 53–77, 2010.
A. X. Dou, X. Q. Wang, and M. W. Dou, “A new approach to evaluate the accuracy of image classification result - r’.” Geoscience and Remote Sensing Symposium, 2004. IGARSS ’04. Proceedings. 2004 IEEE International, 2004
C. Liu, P. Frazier, and L. Kumar, “Comparative assessment of the measures of thematic classification acuracy,” Remote Sensing of En- vironment, pp. 606–616, 2007.
R. Dass, Priyanka, and S. Devi, “Image segmentation techniques,” International Journal of Electronics & Communication Technology IJEC, vol. 3, no. 14, pp. 66–70, March 2012.
R. Adams and L. Bischof, “Seeded region growing,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, no. 6, pp. 641–647, 1994.
J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, Eds., vol. 1, Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 1967, pp. 281–297.
J. C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” 1974.
J. C. Bezdek, “Pattern recognition with fuzzy objective function algo- rithms,” 1981.
Y. Yang and S. Huang, “Image segmentation by fuzzy c-means clustering algorithm with a novel penalty term,” COMPUTING AND INFORMATICS, vol. 26, no. 1, 2007. [Online]. Available: http://www.cai.sk/ ojs/index.php/cai/article/view/296
G. Pajares and J. M. de la Cruz Garcia, Ejercicios resuletos de Vision por Computador. Mexico D.F., 221 Mexico: Alfaomega, 2008.
C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, Sep. 1995. [Online]. Available: http://dx.doi. org/10.1023/A:1022627411411
G. Mountrakis, J. Im, and C. Ogole, “Support vector machines in remote sensing: A review,” {ISPRS} Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247 – 259, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0924271610001140
B. Schlkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, 2001.
A. Gidudu, G. Hulley, and T. Marwala, “Image classification using svms: One-against-one vs oneagainst-all,” CoRR, vol. abs/0711.2914, 2007.
M. Story and R. G. Congalton, “Accuracy assessment - A user’s perspective,” Photogrammetric Engineering and Remote Sensing, vol. 52, no. 3, pp. 397–399, Mar. 1986. [Online]. Available: http:// www.asprs.org/publications/pers/scans/ 1986journal/mar/1986 mar 397-399.pdf
J. Cohen, “A Coefficient of Agreement for Nominal Scales,” Educatio- nal and Psychological Measurement, vol. 20, no. 1, p. 37, 1960.
R. Congalton and R. A. Mead, “A Quantitative Method to Test for Consistency and Correctness in Photointerpretation,” PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, vol. 49, no. 1, pp. 69–74, 1983. [Online]. Available: http://www.citeulike.org/group/7074/article/6012274
J. R. Landis and G. G. Koch, “The Measurement of Observer Agreement for Categorical Data,” Biometrics, vol. 33, no. 1, pp. 159–174, Mar. 1977.
C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 27:1–27:27, 2011, software available at http://www.csie.ntu. edu. tw/cjlin/libsvm.
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