An optimal nonparametric weighted system for hyperspectral data classification

Li-Wei Ko*, Bor Chen Kuo, Ching Teng Lin

*Corresponding author for this work

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

1 Scopus citations

Abstract

In real situation, gathering enough training samples is difficult and expensive. Assumption of enough training samples is usually not satisfied for high dimensional data. Small training sets usually cause Hughes phenomenon and singularity problems. Feature extraction and feature selection are usual ways to overcome these problems. In this study, an optimal classification system for classifying hyperspectral image data is proposed. It is made up of orthonormal coordinate axes of the feature space. Classification performance of the classification system is much better than the other well-known ones according to the experiment results below. It possesses the advantage of using fewer features and getting better performance.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Pages866-872
Number of pages7
DOIs
StatePublished - 1 Dec 2005
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 14 Sep 200516 Sep 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3681 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
CountryAustralia
CityMelbourne
Period14/09/0516/09/05

Keywords

  • Feature extraction
  • Feature selection
  • Hughes phenomenon
  • Pattern recognition

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