Please use this identifier to cite or link to this item:
|Title:||Feature Evolution for Classification of Remotely Sensed Data|
|Authors:||STATHAKIS DIMITRIOS; PERAKIS Kostas|
|Citation:||IEEE Geoscience and Remote Sensing Letters vol. 4 no. 3 p. 354-358|
|Publisher:||IEEE Geoscience and Remote Sensing Society|
|Type:||Articles in periodicals and books|
|Abstract:||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).|
|JRC Institute:||Space, Security and Migration|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.