Mapping Winter Wheat Using Ensemble‐Based Positive Unlabeled Learning Approach

Abstract

High‐resolution remote sensing images can support machine learning methods to achieve remarkable results in agricultural monitoring. However, traditional supervised learning methods require pre-labeled training data and are unsuitable for non-fully labeled areas. Positive and Unlabeled Learning (PUL), can deal with unlabeled data. A loss function PU-Loss was proposed in this study to directly optimize the PUL evaluation metric and to address the data imbalance problem caused by unlabeled positive samples. Moreover, a hybrid normalization module Batch Instance-Layer Normalization was proposed to perform multiple normalization methods based on the resolution size and to improve the model performance further. A real‐world positive and unlabeled winter wheat data set was used to evaluate the proposed method, which outperformed widely used models such as U‐Net, DeepLabv3+, and DA‐Net. The results demonstrated the potential of PUL for winter wheat identification in remote sensing images.