@inproceedings{10900d8883d64614b569b64ffb8f7be7,
title = "Dimensionality reduction and classification based on ant colony algorithm for hyperspectral remote sensing image",
abstract = "This paper proposes a method of dimensionality reduction and classification based on ant colony algorithm for hyperspectral remote sensing image. The high- dimensional hyperspectral data space is decomposed into several lowdimensional data subspace by ant colony algorithm (ACA) in terms of the correlation between bands. Then principal component analysis is used in subspace to extract features, whereafter the classification of hyperspectral image is carried out by maximum likelihood classifier. The experiments show that comparing with the method of dimensionality reduction which doesn't use ACA decomposition (i.e. standard PCA), the method proposed is more reasonable, and reserves more useful information, has the higher classification accuracy.",
keywords = "Ant colony algorithm, Classification, Dimensionality reduction, Feature extraction, Hyperspectral image",
author = "Shuang Zhou and Junping Zhang and Baoku Su",
year = "2008",
doi = "10.1109/IGARSS.2008.4780111",
language = "英语",
isbn = "9781424428083",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",
pages = "V393--V396",
booktitle = "2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings",
address = "美国",
edition = "1",
note = "28th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008 ; Conference date: 06-07-2008 Through 11-07-2008",
}