  Impervious surface area is a direct consequence of the urbanization, which
also plays an important role in urban planning and environmental management.
With the rapidly technical development of remote sensing, monitoring urban
impervious surface via high spatial resolution (HSR) images has attracted
unprecedented attention recently. Traditional multi-classes models are
inefficient for impervious surface extraction because it requires labeling all
needed and unneeded classes that occur in the image exhaustively. Therefore, we
need to find a reliable one-class model to classify one specific land cover
type without labeling other classes. In this study, we investigate several
one-class classifiers, such as Presence and Background Learning (PBL), Positive
Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious
surface area using high spatial resolution imagery of GF-1, China's new
generation of high spatial remote sensing satellite, and evaluate the
classification accuracy based on artificial interpretation results. Compared to
traditional multi-classes classifiers (ANN and SVM), the experimental results
indicate that PBL and PUL provide higher classification accuracy, which is
similar to the accuracy provided by ANN model. Meanwhile, PBL and PUL
outperforms OCSVM, BSVM, MAXENT and SVM models. Hence, the one-class
classifiers only need a small set of specific samples to train models without
losing predictive accuracy, which is supposed to gain more attention on urban
impervious surface extraction or other one specific land cover type.
