Progressive Multiscale Generator for Domain Generalization in Hyperspectral Image Classification With Small Sample

Domain generalization-based hyperspectral image classification methods have achieved promising results in recent years. However, these studies seldom consider the issue of small sample in the source domain. In practical applications, manually annotating hyperspectral images is difficult, so labeled samples in the source domain may be scarce. Existing models have limited feature extraction capability and poor generalization performance in scenarios with limited labeled samples. To address the limitations of existing methods on small sample data of the source domain, a novel approach, Progressive Multiscale Generator for Domain Generalization (PMGDG), is proposed in this paper. The PMGDG employs a progressive multiscale generator comprising a series of sub-generators with paired sub-discriminators. The channel dimension of generated samples grows gradually from the first layer to the last layer. Then, the Classifier network is trained on both the original samples and the generated samples with different distributions to enhance its generalization performance. Additionally, we introduce a hierarchical optimization approach to stabilize the training process. Extensive experiments are conducted on three public hyperspectral image cross-domain datasets:Houston, Pavia, and HyRANK. The experimental results demonstrate that, compared to existing domain generalization methods for hyperspectral image classification, the proposed approach significantly improves classification performance under small sample. The code is available from the website: https://github.com/adwfdawd/PMGDG.