Class Prior-Free Positive-Unlabeled Learning with Taylor Variational
  Loss for Hyperspectral Remote Sensing Imagery
Hengwei Zhao Xinyu Wang Jingtao Li Yanfei Zhong
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing
imagery (HSI) is aimed at learning a binary classifier from positive and
unlabeled data, which has broad prospects in various earth vision applications.
However, when PU learning meets limited labeled HSI, the unlabeled data may
dominate the optimization process, which makes the neural networks overfit the
unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU
learning, which reduces the weight of the gradient of the unlabeled data by
Taylor series expansion to enable the network to find a balance between
overfitting and underfitting. In addition, the self-calibrated optimization
strategy is designed to stabilize the training process. Experiments on 7
benchmark datasets (21 tasks in total) validate the effectiveness of the
proposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.