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EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion

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Autor: García Gutiérrez, Jorge
Mateos García, Daniel
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2012
Publicado en: Neurocomputing, 75 (1), 115-122.
Tipo de documento: Artículo
Resumen: Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking...
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Cita: García Gutiérrez, J., Mateos García, D. y Riquelme Santos, J.C. (2012). EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion. Neurocomputing, 75 (1), 115-122.
Tamaño: 784.9Kb
Formato: PDF

URI: http://hdl.handle.net/11441/43462

DOI: http://dx.doi.org/10.1016/j.neucom.2011.02.020

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