Triple Loss Adversarial Domain Adaptation Network for Cross-Domain Sea–Land Clutter Classification

The existing sea–land clutter classification task of sky-wave over-the-horizon-radar (OTHR) assumes that the training data and test data are drawn from the same probability distribution. However, there is a distribution discrepancy/domain shift of the collected sea–land clutter under various working conditions of OTHR, which leads to the advanced sea–land clutter classification methods being difficult to achieve effective cross-domain classification. To solve this problem, this article proposes an improved maximum classifier discrepancy (MCD) framework, namely, triple loss adversarial domain adaptation network (TLADAN) for cross-domain sea–land clutter classification, which includes a metric-based feature-level loss, an adversarial-based instance-level loss, and an adversarial-based class-level loss. The proposed TLADAN performs feature-, instance-, and class-level alignments of the sea–land clutter from different domains, so as to learn the domain-invariant features to improve the classification performance in the cross-domain scenario. Our method is evaluated in six sea–land clutter domain adaptation (DA) scenarios. Meanwhile, state-of-the-art DA methods are selected for comparison. The experimental results validate the effectiveness and superiority of TLADAN.