Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance. In this study, we propose a robust self-ensembling network (RSEN) to address this problem. The proposed RSEN consists of two subnetworks including a base network and an ensemble network. With the constraint of both the supervised loss from the labeled data and the unsupervised loss from the unlabeled data, the base network and the ensemble network can learn from each other, achieving the self-ensembling mechanism. To the best of our knowledge, the proposed method is the first attempt to introduce the self-ensembling technique into the HSI classification task, which provides a different view on how to utilize the unlabeled data in HSI to assist the network training. We further propose a novel consistency filter to increase the robustness of self-ensembling learning. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed algorithm can yield competitive performance compared with the state-of-the-art methods. Code is available online (\url{https://github.com/YonghaoXu/RSEN}).
翻译:最近的研究显示,在超光谱图像(HSI)分类任务中,深层次学习算法具有巨大的潜力。然而,培训这些模型通常需要大量标签数据。由于收集HSI像素级说明既费力又费时,因此开发能够在小样本规模情况下产生良好业绩的算法非常重要。在本研究中,我们提议建立一个强有力的自我聚合网络(RSEN)来解决这个问题。拟议的RSEN由两个子网络组成,包括一个基础网络和一个集合网络。由于标签数据的监督损失和未标签数据未监督的损失的制约,基础网络和集合网络可以相互学习,从而实现自我集合机制。根据我们的知识,拟议的方法是首次尝试将自我集合技术引入HSI分类任务,这为如何利用HSI的未标记数据协助网络培训提供了不同的观点。我们进一步提议了一个新的一致性过滤器,以提高HRS/NB在线数据的可靠性能性能,而HARS/NGI 测试的测试标准是比较性能标准。