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Label Dependent Evolutionary Feature Weighting for Remote Sensing Data


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Opened Access Label Dependent Evolutionary Feature Weighting for Remote Sensing Data

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Author: Mateos García, Daniel
García Gutiérrez, Jorge
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2010
Published in: Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, Volume 6077, pp 272-279
Document type: Chapter of Book
Abstract: Nearest neighbour (NN) is a very common classifier used to develop important remote sensing products like land use and land cover (LULC) maps. Evolutive computation has often been used to obtain feature weighting in order to improve the results of the NN. In this paper, a new algorithm based on evolutionary computation which has been called Label Dependent Feature Weighting (LDFW) is proposed. The LDFW method transforms the feature space assigning different weights to every feature depending on each class. This multilevel feature weighting algorithm is tested on remote sensing data from fusion of sensors (LIDAR and orthophotography). The results show an improvement on the NN and resemble the results obtained with a neural network which is the best classifier for the study area.
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