Building Cross-Domain Mapping Chains From Multi-CycleGAN for Hyperspectral Image Classification

The small-sample-size issue in hyperspectral image (HSI) classification remains a significant challenge. To improve the classification accuracy of a dataset with a few labeled samples (target domain), we can use knowledge from another dataset with sufficient labeled samples (source domain). This is called cross-domain HSI classification. Transfer learning enables knowledge transfer between source and target domains. Different HSI datasets are often acquired by different sensors, resulting in different characteristics. Consequently, different HSIs possess different feature spaces, and knowledge transfer between them becomes difficult due to heterogeneities. CycleGAN, based on adversarial learning, can help solve heterogeneous transfer learning tasks by establishing the two-way mapping between two different feature spaces. However, CycleGAN contains only one cycle for data, leading to large mapping errors. This article proposes a novel CycleGAN-based transfer learning method for cross-domain HSI classification. The proposed method extends the two-way mapping of CycleGAN. It incorporates multiple mapping cycles to construct a multi-CycleGAN, which is then unfolded to derive the Cross-Domain Mapping Chain (CDMC) model. The generators in our proposed CDMC provide accurate mappings between domains. Moreover, we calculate and accumulate errors in each cycle, and the backpropagation of accumulated errors through the chains improves the model’s performance. Besides, auxiliary classifiers are introduced to account for class-conditional distributions in the mapping process. Experimental results on three real-world heterogeneous cross-domain HSI datasets show the effectiveness of the proposed method.