SRSN: A Semi-Supervised Robust Self-Ensemble Network for Hyperspectral Images Classification

The convolutional neural network (CNN) has promoted hyperspectral images (HSIs) classification performance. However, the size of the convolutional kernel is fixed, whereas the size of objects in HSIs varies greatly; training a CNN requires a large number of samples with label, but manually tagging each pixel of HSIs is time-consuming and labor-intensive. To address above problems, a semi-supervised robust self-ensemble network (SRSN) is proposed in this letter. The SRSN contains a basic network and an ensemble network. The two networks can learn from each other to realize self-ensemble learning. Specifically, the deformable convolution, which is originally applied to the spatial dimension, is extended to the spectral dimension, thereby effectively solves the problem of CNN's fixed convolutional kernel. Concurrently, to enhance the performance of the semi-supervised classifier, a consistency filter is proposed to screen unlabeled samples with high confidence. Experiments were carried out on the international common test datasets. The experimental results fully prove that the SRSN model proposed in this letter is superior to other methods and achieves 97.28%, 82.88%, and 89.13% OA of PaviaCenter, Houston2013, and WHU-Hi-HongHu datasets.