Label dependent evolutionary feature weighting for remote sensing data

Authors:
Daniel Mateos-García;Jorge García-Gutiérrez;José C. Riquelme-Santos
Affiliations:
Department of Computer Science, Seville, (Spain);Department of Computer Science, Seville, (Spain);Department of Computer Science, Seville, (Spain)
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Year:
2010

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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.