  Deep learning (DL)-based sea\textendash land clutter classification for
sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In
engineering applications, real-time predictions of sea\textendash land clutter
with existing distribution discrepancies are crucial. To solve this problem,
this article proposes a novel Multisource Semisupervised Adversarial Domain
Generalization Network (MSADGN) for cross-scene sea\textendash land clutter
classification. MSADGN can extract domain-invariant and domain-specific
features from one labeled source domain and multiple unlabeled source domains,
and then generalize these features to an arbitrary unseen target domain for
real-time prediction of sea\textendash land clutter. Specifically, MSADGN
consists of three modules: domain-related pseudolabeling module,
domain-invariant module, and domain-specific module. The first module
introduces an improved pseudolabel method called domain-related pseudolabel,
which is designed to generate reliable pseudolabels to fully exploit unlabeled
source domains. The second module utilizes a generative adversarial network
(GAN) with a multidiscriminator to extract domain-invariant features, to
enhance the model's transferability in the target domain. The third module
employs a parallel multiclassifier branch to extract domain-specific features,
to enhance the model's discriminability in the target domain. The effectiveness
of our method is validated in twelve domain generalizations (DG) scenarios.
Meanwhile, we selected 10 state-of-the-art DG methods for comparison. The
experimental results demonstrate the superiority of our method.
