Building detection with decision fusion

Caglar Senaras, Mete Ozay, Fatos T. Yarman Vural

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

A novel decision fusion approach to building detection problem in VHR optical satellite images is proposed. The method combines the detection results of multiple classifiers under a hierarchical architecture, called Fuzzy Stacked Generalization (FSG). After an initial segmentation and pre-processing step, a large variety of color, texture and shape features are extracted from each segment. Then, the segments, represented in K different feature spaces are classified by K different base-layer classifiers of the FSG architecture. The class membership values of the segments, which represent the decisions of different base-layer classifiers in a decision space, are aggregated to form a fusion space which is then fed to a meta-layer classifier of the FSG to label the vectors in the fusion space. The paper presents the performance results of the proposed decision fusion model by a comparison with the state of the art machine learning algorithms. The results show that fusing the decisions of multiple classifiers improves the performance, when they are ensembled under the suggested hierarchical learning architecture.

Original languageEnglish
Article number6479321
Pages (from-to)1295-1304
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume6
Issue number3
DOIs
Publication statusPublished - 2013 Mar 19
Externally publishedYes

Keywords

  • Building detection
  • decision fusion
  • ensemble learning
  • fuzzy κ-nearest neighbors classification
  • multi-layer classification
  • segmentation

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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