AUTOMATIC BUILDING DETECTION WITH FEATURE SPACE FUSION USING ENSEMBLE LEARNING

2012-07-27
Senaras, Caglar
Yuksel, Baris
Ozay, Mete
Yarman-Vural, Fatos
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.

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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.