Joint Adversarial Network With Semantic and Topology Fusion for Cross-Scene Hyperspectral Image Classification

Hyperspectral image cross-scene classification (HSICC) poses a significant challenge due to distribution variations between source and target domains. Existing unsupervised domain adaptation methods primarily focus on local knowledge transfer, often neglecting the critical semantic information and sample topological structure inherent in hyperspectral images (HSIs). To address these limitations, this article introduces an end-to-end joint adversarial network with semantic and topology fusion (JAN-STF). This network liberates from the constraints of local perception by integrating semantic and topological information into both domain- and class-level adversarial learning processes. First, the network constructs a semantic-guided cross-domain graph structure to obtain cross-domain features. Subsequently, domain-level adversarial learning is conducted using these features to achieve domain-invariant representation with robust transferability. Moreover, to bolster stability in the ensuing class-level adversarial procedure, the network dynamically computes cross-domain category center distance loss utilizing an intra-domain topological semantic attention mechanism, thereby mapping features to proximate spaces. Finally, class-level adversarial learning is performed by leveraging the prediction discrepancy between the local classifier and the topological classifier, thus enhancing the discriminative performance of the domain-invariant representation. Extensive experiments on three broadly utilized HSICC datasets demonstrate JAN-STF’s superiority in accuracy and Kappa coefficient (KC) metrics over nine leading algorithms.