ABSTRACT Recent research in deep learning has significantly improved the performance of hyperspectral classification. However, nearly all experimental evaluations based on deep networks have taken the form of ‘closed set’, where all testing classes are known in training time. Obviously, it is unreasonable for real-world-applications, where unknown classes in training time may appear during testing. The realistic scenarios require classifiers not only to classify the known classes, but to reject the unknown classes, which is referred as open set classification (OSC). Considering the increased applications in real world, the adaption of deep networks-based methods towards OSC can be of vital importance. Unfortunately, it has not been well addressed by existing deep network-based algorithms. To tackle it, we present a method based on deep networks for hyperspectral OSC, by introducing an OpenMax layer which can estimate the probability of an input being from unknown classes and classify known classes. Experiments conducted on five hyperspectral datasets show that the number of misclassifications of the unknowns made by traditional three-dimensional Convolutional Neural Network (CNN) can be reduced significantly using proposed method. This paper highlights the open set challenge in hyperspectral classification and verifies the effectiveness of OpenMax method for solving such problem.

中文翻译：

---

对深度网络具有未知类别拒绝的高光谱开放集分类

摘要 深度学习的最新研究显着提高了高光谱分类的性能。然而，几乎所有基于深度网络的实验评估都采用“封闭集”的形式，其中所有测试类在训练时间内都是已知的。显然，这对于现实世界的应用来说是不合理的，在测试过程中可能会出现训练时间未知的类。现实场景要求分类器不仅要对已知类进行分类，还要拒绝未知类，这称为开放集分类（OSC）。考虑到现实世界中越来越多的应用，基于深度网络的方法对 OSC 的适应可能至关重要。不幸的是，现有的基于深度网络的算法并没有很好地解决这个问题。为了解决它，我们提出了一种基于深度网络的高光谱 OSC 方法，通过引入 OpenMax 层，该层可以估计输入来自未知类的概率并对已知类进行分类。在五个高光谱数据集上进行的实验表明，使用所提出的方法可以显着减少传统三维卷积神经网络 (CNN) 对未知数的错误分类数量。本文重点介绍了高光谱分类中的开放集挑战，并验证了 OpenMax 方法解决此类问题的有效性。在五个高光谱数据集上进行的实验表明，使用所提出的方法可以显着减少传统三维卷积神经网络 (CNN) 对未知数的错误分类数量。本文重点介绍了高光谱分类中的开放集挑战，并验证了 OpenMax 方法解决此类问题的有效性。在五个高光谱数据集上进行的实验表明，使用所提出的方法可以显着减少传统三维卷积神经网络 (CNN) 对未知数的错误分类数量。本文重点介绍了高光谱分类中的开放集挑战，并验证了 OpenMax 方法解决此类问题的有效性。