Xu Yonghao, Du Bo, Zhang Liangpei
IEEE Trans Neural Netw Learn Syst. 2024 Mar;35(3):3780-3793. doi: 10.1109/TNNLS.2022.3198142. Epub 2024 Feb 29.
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.
最近的研究表明，深度学习算法在高光谱图像（HSI）分类任务中具有巨大潜力。然而，训练这些模型通常需要大量的标注数据。由于为HSI收集像素级注释既费力又耗时，因此开发在小样本量情况下仍能产生良好性能的算法具有重要意义。在本研究中，我们提出了一种鲁棒的自集成网络（RSEN）来解决这一问题。所提出的RSEN由两个子网络组成，包括一个基础网络和一个集成网络。在来自标注数据的监督损失和来自未标注数据的无监督损失的双重约束下，基础网络和集成网络可以相互学习，实现自集成机制。据我们所知，所提出的方法是首次尝试将自集成技术引入HSI分类任务，这为如何利用HSI中的未标注数据辅助网络训练提供了一个不同的视角。我们还提出了一种新颖的一致性滤波器，以提高自集成学习的鲁棒性。在三个基准HSI数据集上进行的大量实验表明，与现有方法相比，所提出的算法能够产生具有竞争力的性能。
