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<i>Pedicularis</i> has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract <i>Pedicularis</i> and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts logistic regression, support vector machine (SVM), and random forest classifiers for multi-class classification. One-class SVM (OCSVM), isolation forest, and positive and unlabeled learning (PUL) algorithms are used for one-class classification. The results are as follows: (1) The accuracy of multi-class classifiers is better than that of one-class classifiers, but it requires all classes that occur in the image to be exhaustively assigned labels. Among the one-class classifiers that only need to label positive or positive and labeled data, the PUL has the highest F score of 0.9878. (2) PUL performs the most robustly to change features in one-class classifiers. All one-class classifiers prove that the green band is essential for extracting <i>Pedicularis</i>. (3) The parameters of the PUL are easy to tune, and the training time is easy to control. Therefore, PUL is a promising one-class classification method for <i>Pedicularis</i> extraction, which can accurately identify the distribution range of <i>Pedicularis</i> to promote grassland administration.