AUTOMATIC BUILDING DETECTION WITH FEATURE SPACE FUSION USING ENSEMBLE LEARNING

Senaras C., Yuksel B., Ozay M., Yarman-Vural F.

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22 - 27 July 2012, pp.6713-6716 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/igarss.2012.6352058
  • City: Munich
  • Country: Germany
  • Page Numbers: pp.6713-6716
  • Keywords: remote sensing, pattern recognition, multi-layer classification, stacked generalization, building detection, decision fusion, fuzzy k-nn classification, PERFORMANCE, IMAGES
  • Middle East Technical University Affiliated: No

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.