Automatic building detection with feature space fusion using ensemble learning

Çaǧlar Şenaras, Bariş Yüksel, Mete Özay, Fatoş Yarman-Vural

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

This paper proposes a novel approach to building detection problem in satellite images. The proposed method employs a two layer hierarchical classification mechanism for ensemble learning. After an initial segmentation, each segment is classified by N different classifiers using different features at the first layer. The class membership values of the segments, which are obtained from different base layer classifiers, are ensembled to form a new fusion space, which forms a linearly separable simplex. Then, this simplex is partitioned by a linear classifier at the meta layer. The paper presents the performance results of the proposed model and comparisons with the state of the art classifiers.

Original languageEnglish
Pages6713-6716
Number of pages4
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
Duration: 2012 Jul 222012 Jul 27

Other

Other2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
CountryGermany
CityMunich
Period12/7/2212/7/27

Keywords

  • building detection
  • decision fusion
  • fuzzy k-nn classification
  • multi-layer classification
  • pattern recognition
  • remote sensing
  • stacked generalization

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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