  Recently, self-supervised learning has attracted attention due to its
remarkable ability to acquire meaningful representations for classification
tasks without using semantic labels. This paper introduces a self-supervised
learning framework suitable for hyperspectral images that are inherently
challenging to annotate. The proposed framework architecture leverages
cross-domain CNN, allowing for learning representations from different
hyperspectral images with varying spectral characteristics and no pixel-level
annotation. In the framework, cross-domain representations are learned via
contrastive learning where neighboring spectral vectors in the same image are
clustered together in a common representation space encompassing multiple
hyperspectral images. In contrast, spectral vectors in different hyperspectral
images are separated into distinct clusters in the space. To verify that the
learned representation through contrastive learning is effectively transferred
into a downstream task, we perform a classification task on hyperspectral
images. The experimental results demonstrate the advantage of the proposed
self-supervised representation over models trained from scratch or other
transfer learning methods.
