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Impervious surface area is a direct consequence of the urbanization, whichalso plays an important role in urban planning and environmental management.With the rapidly technical development of remote sensing, monitoring urbanimpervious surface via high spatial resolution (HSR) images has attractedunprecedented attention recently. Traditional multi-classes models areinefficient for impervious surface extraction because it requires labeling allneeded and unneeded classes that occur in the image exhaustively. Therefore, weneed to find a reliable one-class model to classify one specific land covertype without labeling other classes. In this study, we investigate severalone-class classifiers, such as Presence and Background Learning (PBL), PositiveUnlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervioussurface area using high spatial resolution imagery of GF-1, China's newgeneration of high spatial remote sensing satellite, and evaluate theclassification accuracy based on artificial interpretation results. Compared totraditional multi-classes classifiers (ANN and SVM), the experimental resultsindicate that PBL and PUL provide higher classification accuracy, which issimilar to the accuracy provided by ANN model. Meanwhile, PBL and PULoutperforms OCSVM, BSVM, MAXENT and SVM models. Hence, the one-classclassifiers only need a small set of specific samples to train models withoutlosing predictive accuracy, which is supposed to gain more attention on urbanimpervious surface extraction or other one specific land cover type.

Extracting urban impervious surface from GF-1 imagery using one-class classifiers