Unsupervised Domain Adaptation With Content-Wise Alignment for Hyperspectral Imagery Classification

Unsupervised domain adaptation (UDA) attempts to boost the performance on an unlabeled target domain by transferring knowledge from a labeled source domain. The previous models consider domain-level discrepancy while neglecting content-level distinction. To further decrease the distribution gap between different domains, this letter proposes a novel UDA approach with content-wise alignment for hyperspectral image classification (HSIC). We accomplish feature alignment with content-wise discrepancy reduction through an adversarial framework for the first time. Expressly, the core of the proposed content-wise scheme is integrated with a class-level and style-perceive-level regularized alignment to strengthen the representation of invariant feature. The experimental analysis demonstrates that the proposed model achieves more effective performance than other domain adaptation methods for hyperspectral image (HSI).

Automated summary

The study introduces a novel unsupervised domain adaptation approach with content-wise alignment to decrease the distribution gap between different domains, achieving more effective performance for hyperspectral image classification.