  Currently, cross-scene hyperspectral image (HSI) classification has drawn
increasing attention. It is necessary to train a model only on source domain
(SD) and directly transferring the model to target domain (TD), when TD needs
to be processed in real time and cannot be reused for training. Based on the
idea of domain generalization, a Single-source Domain Expansion Network
(SDEnet) is developed to ensure the reliability and effectiveness of domain
extension. The method uses generative adversarial learning to train in SD and
test in TD. A generator including semantic encoder and morph encoder is
designed to generate the extended domain (ED) based on
encoder-randomization-decoder architecture, where spatial and spectral
randomization are specifically used to generate variable spatial and spectral
information, and the morphological knowledge is implicitly applied as domain
invariant information during domain expansion. Furthermore, the supervised
contrastive learning is employed in the discriminator to learn class-wise
domain invariant representation, which drives intra-class samples of SD and ED.
Meanwhile, adversarial training is designed to optimize the generator to drive
intra-class samples of SD and ED to be separated. Extensive experiments on two
public HSI datasets and one additional multispectral image (MSI) dataset
demonstrate the superiority of the proposed method when compared with
state-of-the-art techniques.
