AUTOMATIC BUILDING DETECTION WITH FEATURE SPACE FUSION USING ENSEMBLE LEARNING

Date

2012-07-27

Author

Senaras, Caglar
Yuksel, Baris
Ozay, Mete
Yarman-Vural, Fatos

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Remote sensing, Pattern recognition, Multi-layer classification, Stacked generalization, Building detection, Decision fusion, Fuzzy k-nn classification

URI

https://hdl.handle.net/11511/67348

DOI

https://doi.org/10.1109/igarss.2012.6352058

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

C. Senaras, B. Yuksel, M. Ozay, and F. Yarman-Vural, “AUTOMATIC BUILDING DETECTION WITH FEATURE SPACE FUSION USING ENSEMBLE LEARNING,” 2012, p. 6713, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67348.