Building Detection With Decision Fusion

Date

2013-6

Author

Senaras, Caglar
Ozay, Mete
Yarman Vural, Fatoş Tunay

Metadata

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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.

Subject Keywords

Building detection, Segmentation, Multi-layer classification, Ensemble learning, Decision fusion, Fuzzy kappa-nearest neighbors classification

URI

https://hdl.handle.net/11511/28379

Journal

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI

https://doi.org/10.1109/jstars.2013.2249498

Collections

Department of Computer Engineering, Article

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Citation Formats

C. Senaras, M. Ozay, and F. T. Yarman Vural, “Building Detection With Decision Fusion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1295–1304, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28379.