  Text information including extensive prior knowledge about land cover classes
has been ignored in hyperspectral image classification (HSI) tasks. It is
necessary to explore the effectiveness of linguistic mode in assisting HSI
classification. In addition, the large-scale pre-training image-text foundation
models have demonstrated great performance in a variety of downstream
applications, including zero-shot transfer. However, most domain generalization
methods have never addressed mining linguistic modal knowledge to improve the
generalization performance of model. To compensate for the inadequacies listed
above, a Language-aware Domain Generalization Network (LDGnet) is proposed to
learn cross-domain invariant representation from cross-domain shared prior
knowledge. The proposed method only trains on the source domain (SD) and then
transfers the model to the target domain (TD). The dual-stream architecture
including image encoder and text encoder is used to extract visual and
linguistic features, in which coarse-grained and fine-grained text
representations are designed to extract two levels of linguistic features.
Furthermore, linguistic features are used as cross-domain shared semantic
space, and visual-linguistic alignment is completed by supervised contrastive
learning in semantic space. Extensive experiments on three datasets demonstrate
the superiority of the proposed method when compared with state-of-the-art
techniques.
