IEICE TRANSACTIONS on Information
Particle Swarms for Feature Extraction of Hyperspectral Data
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.7 pp.1038-1046
- Publication Date
- 2007/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.7.1038
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Sildomar Takahashi MONTEIRO, Yukio KOSUGI, "Particle Swarms for Feature Extraction of Hyperspectral Data" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 7, pp. 1038-1046, July 2007, doi: 10.1093/ietisy/e90-d.7.1038.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.7.1038/_p
Copy
@ARTICLE{e90-d_7_1038,
author={Sildomar Takahashi MONTEIRO, Yukio KOSUGI, },
journal={IEICE TRANSACTIONS on Information},
title={Particle Swarms for Feature Extraction of Hyperspectral Data},
year={2007},
volume={E90-D},
number={7},
pages={1038-1046},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e90-d.7.1038},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Particle Swarms for Feature Extraction of Hyperspectral Data
T2 - IEICE TRANSACTIONS on Information
SP - 1038
EP - 1046
AU - Sildomar Takahashi MONTEIRO
AU - Yukio KOSUGI
PY - 2007
DO - 10.1093/ietisy/e90-d.7.1038
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2007
AB - 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.
ER -
English
