Spatial-Spectral Contrastive Learning for Hyperspectral Image Classification

In spite of being widely used in hyperspectral image (HSI) classification, most deep learning algorithms require plenty of labeled samples to achieve satisfactory performance. However, manual labeling is very time-consuming and laborious in practice. To solve such problem, we propose a method, called spatial-spectral contrastive learning (SSCL), to learn the representations of HSIs suitable for classification in an unsuper-vised manner. We demonstrate that the useful contents (e.g., semantics) are invariant under the spatial and spectral domains while the uninformative ones are usually not. Thus, we learn powerful representations that model domain-invariant information by defining a contrastive prediction task. Specifically, two signals are constructed for an HSI sample to include information of the two domains, and the representations of these two signals are then optimized to be similar, such that the domain-invariant contents are extracted. We conduct classification experiments on the learned representation with very few labels, the results of which verify the superiority of our method over the state-of-the-art techniques.