Estimating mixing parameters of regularized feature extractions by genetic algorithm

Bor Chen Kuo*, Kuang Yu Chang, Jinn Min Yang, Li-Wei Ko

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication25th Anniversary IGARSS 2005
Subtitle of host publicationIEEE International Geoscience and Remote Sensing Symposium
Pages3902-3904
Number of pages3
DOIs
StatePublished - 1 Dec 2005
Event2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 - Seoul, Korea, Republic of
Duration: 25 Jul 200529 Jul 2005

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume6

Conference

Conference2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005
CountryKorea, Republic of
CitySeoul
Period25/07/0529/07/05

Keywords

  • Feature extraction
  • Hyperspectral data classification
  • Regularization

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