Label Dependent Evolutionary Feature Weighting for Remote Sensing Data
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 evol...
| Autores: | , , |
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| Tipo de documento: | capítulo de livro |
| Estado: | Versão publicada |
| Data de publicação: | 2010 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositório: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/40523 |
| Acesso em linha: | http://hdl.handle.net/11441/40523 https://doi.org/10.1007/978-3-642-13803-4_34 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Remote sensing Feature weighting Evolutionary computation Label dependence |
| Resumo: | 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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