Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce the requirements for annotation. However, when one-class classification meets HSI, it is difficult for classifiers to find a balance between the overfitting and underfitting of positive data due to the problems of distribution overlap and distribution imbalance. Although deep learning-based methods are currently the mainstream to overcome distribution overlap in HSI multiclassification, few studies focus on deep learning-based HSI one-class classification. In this article, a weakly supervised deep HSI one-class classifier, namely, HOneCls, is proposed, where a risk estimator,the one-class risk estimator, is particularly introduced to make the fully convolutional neural network (FCN) with the ability of one class classification in the case of distribution imbalance. Extensive experiments (20 tasks in total) were conducted to demonstrate the superiority of the proposed classifier.

翻译：高光谱图像（HSI）单类分类旨在仅利用已知的正类数据从HSI中识别单一目标类别，这可以显著降低对标注的需求。然而，当单类分类与HSI结合时，由于数据分布重叠和分布不平衡问题，分类器难以在正类数据的过拟合与欠拟合之间找到平衡。尽管基于深度学习的方法当前是克服HSI多分类中分布重叠的主流，但针对基于深度学习的HSI单类分类的研究较为稀少。本文提出了一种弱监督的深度HSI单类分类器，即HOneCls，其中特别引入了风险估计器——单类风险估计器，使得全卷积神经网络（FCN）在分布不平衡情况下具备单类分类能力。通过大量实验（共计20项任务）验证了所提分类器的优越性。