Triple Loss Adversarial Domain Adaptation Network for Cross-Domain Sea–Land Clutter Classification | IEEE Journals & Magazine | IEEE Xplore

Triple Loss Adversarial Domain Adaptation Network for Cross-Domain Sea–Land Clutter Classification | IEEE Journals & Magazine | IEEE Xplore: 

X. Zhang, Y. Li, Q. Pan and C. Yu, "Triple Loss Adversarial Domain Adaptation Network for Cross-Domain Sea–Land Clutter Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023, Art no. 5110718, doi: 10.1109/TGRS.2023.3328302

Abstract: 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.

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10299709&isnumber=10006360