Ponencia
Remote mining: from clustering to DTM
Autor/es | García Gutiérrez, Jorge
Martínez Álvarez, F. Laguna Ruiz, D. Riquelme Santos, José Cristóbal |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2008 |
Fecha de depósito | 2022-12-12 |
Publicado en |
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ISBN/ISSN | 978-0-85538-774-7 |
Resumen | LIDAR data acquisition is becoming an indispensable task for terrain characterization in large
surfaces. In Mediterranean woods this job results hard due to the great variety of heights and
forms, as well as sparse ... LIDAR data acquisition is becoming an indispensable task for terrain characterization in large surfaces. In Mediterranean woods this job results hard due to the great variety of heights and forms, as well as sparse vegetation that they present. A new data mining-based approach is proposed with the aim of classifying LIDAR data clouds as a first step in DTM generation. The developed methodology consists in a multi-step iterative process that splits the data into different classes (ground and low/med/high vegetation) by means of a clustering algorithm. This method has been tested on three different areas of the southern Spain with successful results, verging on 80% hits |
Agencias financiadoras | Ministerio de Ciencia Y Tecnología (MCYT). España |
Identificador del proyecto | TIN2007-68084 |
Cita | García Gutiérrez, J., Martínez Álvarez, F., Laguna Ruiz, D. y Riquelme Santos, J.C. (2008). Remote mining: from clustering to DTM. En SilviLaser 2008: 8th international conference on LiDAR applications in forest assessment and inventory (389-397), Edinburgh, UK: Bournemouth University. |
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