ABSTRACT

Using deep learning, the supervised hyperspectral image (HSI) classification methods are based on the ideal assumption of closed sets, in which all testing classes are defined a priori. However, it is impossible to collect all classes while training in the open set setting where unknown classes can be submitted while testing. Traditional deep neural networks lack the ability to perceive the unknown classes without external supervision; therefore, they will misclassify them as known classes. This problem is called the open set classification (OSC). This paper proposes a distance-based OSC method for deep neural networks to solve the problem. The method introduces the class anchor clustering (CAC) classifier and loss to train deep neural networks, which can make known classes cluster around fixed class centres in the logit space. Boxplot and extreme value theory (EVT) are then used to determine the distance between input instances and their nearest class centres for unknown rejection without sacrificing the classification accuracy of known classes dramatically. Many experiments on HSI datasets indicated the validity of the HSI–OSC method.

摘要

使用深度学习，有监督的高光谱图像（HSI）分类方法是基于封闭集的理想假设，在该假设中，先验定义了所有测试类别。但是，在开放集设置中进行训练时不可能收集所有课程，在开放设置中可以在测试时提交未知的课程。传统的深度神经网络缺乏在没有外部监督的情况下感知未知类的能力。因此，它们会将它们误分类为已知类。此问题称为开放集分类（OSC）。本文提出了一种基于距离的深度神经网络OSC方法来解决该问题。该方法引入了类锚聚类（CAC）分类器和损失来训练深度神经网络，这可以使已知类围绕Logit空间中的固定类中心进行聚类。然后使用箱线图和极值理论（EVT）确定输入实例与其最近的类中心之间的距离，以进行未知拒绝，而不会显着牺牲已知类的分类精度。在HSI数据集上进行的许多实验表明，HSI–OSC方法是有效的。