Semisupervised deep learning using consistency regularization and pseudolabels for hyperspectral image classification

Hyperspectral image (HSI) classification is a focus area in remote sensing research, wherein redundant spectral information poses a significant challenge and deep-learning-based classifiers have achieved better performance than traditional methods have. Training a deep-learning-based classifier requires numerous labeled samples. However, collecting such a substantial amount of labeled hyperspectral data is difficult. Semisupervised classification of HSIs has thus received increasing attention, where semisupervised learning classifiers function based on labeled and unlabeled data. A new training method for semisupervised HSI classification is proposed. Specifically, consistency regularization and pseudolabeling are combined as a semisupervised training framework, without the introduction of a complex mechanism. Our proposed algorithm can work without the need to change the conventional convolutional neural network model architecture. Unlike previous deep-learning-based methods, our approach does not require data reconstruction to obtain unsupervised loss. This means that our model can be much less computationally intensive. From the results of experiments on three public hyperspectral datasets, our proposed method outperforms several state-of-the-art methods.