Due to the complexity of building composition and imaging condition, urban areas show complicated structural
information in remotely sensed images. On the other hand, the structural information of each site in urban area depends
on that of neighboring sites. In this paper, a discriminative model, conditional random field (CRF), is introduced to learn
the dependencies and fuse the multi-scale textural information to detect urban areas. In addition, because of the
redundancy in structural information, a feature selection method is employed to reduce the dimension of them before
they are put into CRF model, decreasing time consumed in model learning and inferring. By using images of high spatial
resolution as input, experiments are performed, indicating that CRF model outperforms SVM in urban areas detection in
terms of accuracy, and that, through feature selection it can decrease time consumed in model learning and inferring and
obtain competitive result with original data.
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