@inproceedings{a061b116bc3a4c52bc7ef0f97f0fd20a,
title = "Estimating mixing parameters of regularized feature extractions by genetic algorithm",
abstract = "Many researches show that regularization feature extraction can mitigate the Hough phenomena and the singularity effect for hyperspectral data classification. But how to estimate the regularization mixing parameters efficiently is still a problem. In this paper, a mixing parameter estimation method based on genetic algorithm is proposed. Real hyperspectral data experiment results show that the proposed algorithm can estimating mixing parameters with less computation time than traditional grid method.",
keywords = "Feature extraction, Hyperspectral data classification, Regularization",
author = "Kuo, {Bor Chen} and Chang, {Kuang Yu} and Yang, {Jinn Min} and Li-Wei Ko",
year = "2005",
doi = "10.1109/IGARSS.2005.1525763",
language = "English",
isbn = "0780390504",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
pages = "3902--3904",
booktitle = "25th Anniversary IGARSS 2005",
note = "2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 ; Conference date: 25-07-2005 Through 29-07-2005",
}