Investigating the effects of ensemble classification on remotely sensed data for land cover mapping

Bolanle Abe, Anthony Gidudu, Tshilidzi Marwala

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

5 Citations (Scopus)

Abstract

Ensemble classification involves consulting experts in taking final decision in classification process. The idea is to improve classification accuracy when compared to their single classifier counterpart. The system is used in remote sensing imagery to obtain information about Land cover. Major challenges associated with classification accuracy include design procedure of classifier, choice of training sets from dataset and information conveyed to the algorithm. Superiority of different classification approaches employed depends on selected dataset and the strategy used during designing phase of each classifier. However, in ensemble approach, there is no definite number of classifiers that should take part in decision making. This study exploits feature selection technique to create diversity in ensemble classification. Results obtained show that for ensemble approach, there is no significant benefit in having many base classifiers. The outcome should reveal how to design best ensemble using feature selection approach for land cover mapping.

Original languageEnglish
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2832-2835
Number of pages4
ISBN (Print)9781424495658, 9781424495665
DOIs
Publication statusPublished - 2010
Event2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, United States
Duration: 25 Jul 201030 Jul 2010

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Country/TerritoryUnited States
CityHonolulu
Period25/07/1030/07/10

Keywords

  • Classification
  • Feature selection
  • Land cover
  • Mapping
  • Remote sensing

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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