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Positive-unlabeled learning (PU learning) in hyperspectral remote sensingimagery (HSI) is aimed at learning a binary classifier from positive andunlabeled data, which has broad prospects in various earth vision applications.However, when PU learning meets limited labeled HSI, the unlabeled data maydominate the optimization process, which makes the neural networks overfit theunlabeled data. In this paper, a Taylor variational loss is proposed for HSI PUlearning, which reduces the weight of the gradient of the unlabeled data byTaylor series expansion to enable the network to find a balance betweenoverfitting and underfitting. In addition, the self-calibrated optimizationstrategy is designed to stabilize the training process. Experiments on 7benchmark datasets (21 tasks in total) validate the effectiveness of theproposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.

Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery