# Single-Class Data Descriptors for Mapping
## 1 Introduction
The application of remote-sensing techniques plays a significant role in the quantitative resource survey of TCM, particularly in exploring monitoring abilities for the sustainable utilization and bio-diversity protection of Chinese materia media resources in macrocosm. Under the prerequisite that remote-sensing techniques provide up-to-date landscape surveying at a fine scale [31], one of the motivations for this work comes from the fact that only a single class of interest is involved in a mapping task. Recently, there has been a number of applications [32], i.e., geological products [33], vegetation indices [34], aerosol products [35], ocean data products [36], dust source identification [37], and crops identification [38] in remote-sensing based on machine learning techniques. In this study, we present an original innovation to apply most of the available single-class data descriptors (SCDDs) through P-learning to conduct mapping of _Panax notoginseng_ fields. After that, the work of measuring performance will give us profound insights into defining the selection criteria and scoring proof for choosing a fine SCDD. As such, the introduction of SCDDs for remote-sensing mapping of _Panax notoginseng_ fields will be helpful to promoting the development of a _Panax notoginseng_ resource inventory and dynamic monitoring towards the quantitative direction. Attributing to the standardized cultivation technique of good agricultural practice (GAP) [39], or so-called controlled-environment agriculture using shade houses provide a distinct image texture to interpret _Panax notoginseng_ fields visually [40]. Additionally, due to only _Panax notoginseng_ fields being the target class, the task of mapping _Panax notoginseng_ fields becomes a specific typology of land-cover classification, which could be regarded as a problem of single-class data description or a special type of one-class classification [41]. SCDDs are the appealing alternatives to the conventional supervised classifiers because they can be trained with only the target training samples [42]. These kinds of algorithms have emerged to only require training samples from the target class, which are referred to as P-learning [43]. Notice that the single class meant no more than one landscape class, and the P-learning based class description may depict the sight that no negative samples are used for training. Such a classification approach aims to identify only one landscape class of interest regardless of the other classes presented in the study area [44]. In the case of single-class data description, we always face an imbalanced binary classification [45; 46] including (1) the positive class (i.e., _Panax notoginseng_ fields); and (2) the negative class (i.e., the other classes). In this case, the positive class is assumed to be sampled well, while the other classes may be sampled very sparsely or totally absent. When no samples of the other classes are available, most classification errors (e.g., false negative) cannot be estimated [47]. In addition, the procedure that trains an accurate SCDD is challenging, particularly in the face of a large number of unlabeled samples; or say, only a small class or relatively few training samples are available [42]. Therefore, it might be very expensive to collect the negative samples which are so abundant that a good sampling seems elusive. Although this was an extreme case, we carefully designed the training and test sets, which were composed of the qualified positive and negative samples. Note that if we want to improve the overall performance of the numerous classifiers which may differ in complexity, a combination of these classifiers will always be a viable solution [48]. Regarding another motivation of this work with respect to TCM, the quality control of TCM remains a significant issue that affects medicinal herbs, formulations, and even TCM practice.