Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning

Abstract

Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, Panax notoginseng has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese materia medica resources, particularly of Panax notoginseng. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the Panax notoginseng field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of Panax notoginseng fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of Panax notoginseng could be continuously derived in the local area on the basis of SCDDs.

Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

“…Deng and Pu [4] in their paper entitled "Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning" mapped Panax notoginseng fields through a stack of single-class data descriptors (SCDDs) as the future technical milestone for planting pattern analysis, evaluated the abilities of SCDDs in identifying small Panax notoginseng fields in the complex agricultural landscapes, and provided the potential possibilities for monitoring the planting pattern changes of Panax notoginseng fields, further giving us new insights into the planting pattern transitions of the perennial ginseng in macrocosm.…”

A Systematic Literature Review on Machine Learning in Shared Mobility

“…The focus of this review on the methodological considerations of ML is deliberate and informed by several compelling factors. Firstly, ML's data-driven approach is particularly suited for the dynamic and fast-paced nature of shared mobility, enabling more adaptive solutions [32,33]. Secondly, its capability to handle multi-dimensional challenges makes it ideal for the complex systems inherent in shared mobility [34,35].…”