Fuzzy-fusion approach for land cover classification

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

6 Citations (Scopus)

Abstract

The use of computational intelligent techniques for feature extraction and classification from earth observation satellite images, like Landsat multispectral images, can contribute to improve remote sensing analysis. Image fusion techniques are applied to fuse the spectral images into a higher-level image of the land cover distribution. In this paper we propose a fuzzy-fusion inference approach for satellite image classification based on a fuzzy process, which uses both a hybrid method to train the classifier and reinforcement aggregation operators in the inference scheme. The approach was tested with land cover maps for the district of Mandimba of the Niassa province, Mozambique and was validated against an expert classification and then with Decision trees and Artificial Neural Networks.

Original languageEnglish
Title of host publicationINES 2016 - 20th Jubilee IEEE International Conference on Intelligent Engineering Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages177-182
Number of pages6
ISBN (Electronic)978-1-5090-1216-9
DOIs
Publication statusPublished - 26 Aug 2016
Event20th Jubilee IEEE International Conference on Intelligent Engineering Systems, INES 2016 - Budapest, Hungary
Duration: 30 Jun 20162 Jul 2016

Publication series

NameIEEE International Conference on Intelligent Engineering Systems
PublisherIEEE
ISSN (Electronic)1562-5850

Conference

Conference20th Jubilee IEEE International Conference on Intelligent Engineering Systems, INES 2016
CountryHungary
CityBudapest
Period30/06/162/07/16

Keywords

  • FULL-REINFORCEMENT OPERATORS
  • DECISION
  • AGGREGATION
  • ALGORITHMS
  • SYSTEMS
  • IMAGES
  • FOREST

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