Hyperspectral Open Set Classification towards Deep Networks Based on Boxplot

Abstract

Recently, hyperspectral imaging (HSI) supervised classification has achieved an astonishing performance by using deep learning. However, most of them take the ideal assumption of ‘closed set’, where all testing classes have been known during training. In fact, in the real world, new classes unseen in training may appear during testing. Obviously, traditional supervised classification methods cannot operate correctly in the real world, which requires classifiers not only to classify known classes, but reject the unknown in order to avoid false positives. This challenge is called ‘open set classification’(OSC). Considering the increased applications of deep learning in the real world, rejecting unknown classes during classification is of vital importance. To tackle it, we present a simple but effective HSI OSC method toward deep networks. In this method, we tighten the decision boundaries of SoftMax function of the last layer of the deep networks by using boxplots to analysis the statistical characteristics of the probability distribution of known classes and generate a proper rejection threshold for each known class. To test the performance of the proposed HSI OSC method, experiments are conducted on three HSI datasets. The results show that the proposed method outperforms existing state-of-the-art HSI OSC methods.