Capítulo de Libro
A Non-parametric Approach for Accurate Contextual Classification of LIDAR and Imagery Data Fusion
Autor/es | García Gutiérrez, Jorge
Mateos García, Daniel Riquelme Santos, José Cristóbal |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2012 |
Fecha de depósito | 2016-06-23 |
Publicado en |
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ISBN/ISSN | 978-3-642-28930-9 0302-9743 |
Resumen | Light Detection and Ranging (LIDAR) has become a very
important tool to many environmental applications. This work proposes
to use LIDAR and image data fusion to develop high-resolution thematic
maps. A novel methodology ... Light Detection and Ranging (LIDAR) has become a very important tool to many environmental applications. This work proposes to use LIDAR and image data fusion to develop high-resolution thematic maps. A novel methodology is presented which starts building a matrix of statistics from spectral and spatial information by feature extraction on the available bands (RGB from images, and intensity and height from LIDAR). Then, a contextual classification is applied to generate the final map using a support vector machine (SVM) to classify every cell and the nearest neighbor (NN) rule to sequentially reclassify each cell. The results obtained by this novel method, called SVMNNS (SVM and NN Stacking), are compared with non-contextual and contextual SVMs. It is shown that SVMNNS obtains the best results when applied to real data from the Iberian peninsula. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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A non-parametric.pdf | 554.9Kb | [PDF] | Ver/ | |