Building Detection With Decision Fusion

Senaras C., Ozay M., YARMAN VURAL F. T.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol.6, no.3, pp.1295-1304, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 6 Issue: 3
  • Publication Date: 2013
  • Doi Number: 10.1109/jstars.2013.2249498
  • Journal Name: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1295-1304
  • Keywords: Building detection, segmentation, multi-layer classification, ensemble learning, decision fusion, fuzzy kappa-nearest neighbors classification, OBJECT EXTRACTION, MEAN-SHIFT, URBAN-AREA, CLASSIFICATION, IMAGES
  • Middle East Technical University Affiliated: Yes

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 different feature spaces are classified by 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.