  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.
