Self-Supervised Learning With a Dual-Branch ResNet for Hyperspectral Image Classification

Deep learning methods have made considerable progress in many fields, but most of them rely on a large amount of sample. In the hyperspectral image (HSI) classification task, many unlabeled data and few labeled data exist, so it is necessary to use a small number of training samples to achieve good results. In this letter, in order to fuse spectral and spatial information, a dual-branch residual neural network (ResNet) is proposed, with one branch for extracting spectral features and one branch for extracting patch features. Further, according to the properties of the HSI, self-supervised learning training methods are designed for these two branches. When spectral information is used for training, the image is artificially divided into several parts, with each part being a category for the classification task. When patch features are used for training, the task is to recover the spectral information of the intermediate pixels. After the pretext task training is completed, a pre-training weight will be provided for classification task training. Experiments with a small number of samples of two public datasets show that this method has better classification performance than existing methods.