Name
Abstract | ||
|---|---|---|
In this paper, ant colony optimization (ACO) is applied to hyperspectral band selection. The objective is to select a small band subset such that classification accuracy can be maintained or even improved. The ACO-based band selection technique in this research is independent of any classifier, resulting in lower computational cost. Both supervised (i.e., Jeffries-Matusita distance) and unsupervised (i.e., simplex volume) selection criteria are investigated. The experimental results demonstrate that the classification accuracy on the selected bands is higher than using all bands, and ACO-based methods can outperform the widely used sequential forward selection (SFS) method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/WHISPERS.2013.8080641 | 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | Field | DocType |
Ant colony optimization,band selection,classification,hyperspectral image | Ant colony optimization algorithms,Band selection,Pattern recognition,Computer science,Hyperspectral imaging,Simplex,Feature extraction,Linear programming,Artificial intelligence,Classifier (linguistics),Forward selection,Machine learning | Conference |
ISSN | ISBN | Citations |
2158-6268 | 978-1-5090-1120-9 | 0 |
PageRank | References | Authors |
0.34 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianwei Gao | 1 | 11 | 2.53 |
| Qian Du | 2 | 2833 | 185.90 |
| Lianru Gao | 3 | 373 | 59.90 |
| Xu Sun | 4 | 37 | 10.14 |
| Yuanfeng Wu | 5 | 20 | 5.61 |
| Bing Zhang 0001 | 6 | 22 | 7.16 |