Computer processing of remotely-sensed images: an introduction
Computer processing of remotely-sensed images: an introduction
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Swarm intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Feature Transformation and Subset Selection
IEEE Intelligent Systems
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
An introduction to variable and feature selection
The Journal of Machine Learning Research
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Feature selection with particle swarms
CIS'04 Proceedings of the First international conference on Computational and Information Science
Feature selection in regression tasks using conditional mutual information
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
| Hi-index | 0.00 |
This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.