Segmentation and classification of SAR imagery on flood zones in Colombia, a computing tool for disaster prevention

In order to prevent natural flood disasters it important to identify the flood areas. In Colombia, there is space to develop automatic tools able to detect and study flood areas. For this reason, in this work we propose a computational tool in MATLAB, able to detect and classify Colombia’s flood zones in SAR imager. In particular, we used different classifiers, and according to the performance we selected the best. The training database was generated with the results of Fuzzy Clustering, K -means and Region -Growing segmentations on flood zones in SAR imagery. We used two different classifiers: the first one is a Bayes classifier, while the second one is a Support Vector Machine (SVM). In order to evaluate the performance, we used indices such as the overall accuracy, user accuracy and Kappa index. According to the results, the SVM classifier presents better accuracy. However, the Bayes classifier had better results classifying pixels corresponding to populations even with little training data.

• Autores: Avendaño Pérez, Jonathan; Parra Plazas, Jaime Alberto; Bayona, Jhon Fredy
• Tipo de recurso: Article of journal
• Fecha de publicación: 2014
• Institución: Universidad Antonio Nariño
• Repositorio: Repositorio UAN
• Idioma: spa
• OAI Identifier: oai:repositorio.uan.edu.co:123456789/3936
• Acceso en línea: http://revistas.uan.edu.co/index.php/ingeuan/article/view/365; http://repositorio.uan.edu.co/handle/123456789/3936
• Palabra clave: SAR; Classification; Segmentation; flood areas imagery; SAR; Clasificación; Segmentación; imágenes de zonas de inundación
• Rights: openAccess
• License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)