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Many traditional target extraction methods encountered new challenges as the spatial resolution is increasing quickly. For the purpose of extracting buildings in that circumstance, a new method combing both the object-based approach and boosting algorithm is proposed in this paper. The method associates segmentation with recognition by constructing a hierarchical object network, which effectively improves the problem of detecting targets with a modifiable sliding window existed in other methods. And some useful features are selected automatically to train a validate classifier. Then the label confidence of each object is computed using contextual models to complete the extraction procedure. Competitive results for both multiform and complicated buildings demonstrate the precision, robustness and effectiveness of the proposed method.