Feature Evolution for Classification of Remotely Sensed Data
2007
STATHAKIS Dimitrios | PERAKIS Kostas
In a number of remote sensing applications it is critical to decrease the dimensionality of the input in order to reduce the complexity and hence the processing time and possibly improve classification accuracy. In this paper the application of genetic algorithms as a means of feature selection is explored. A genetic algorithm is used to select a near-optimal subset of input dimensions using a feed forward multilayer perceptron trained by backpropagation as the classifier. Feature and topology evolution are performed simultaneously based on actual classification results (wrapper approach).
Afficher plus [+] Moins [-]JRC.G.3 - Agriculture
Afficher plus [+] Moins [-]Informations bibliographiques
Cette notice bibliographique a été fournie par European Union
Découvrez la collection de ce fournisseur de données dans AGRIS
