ICP-Net: Exploiting Iterative Consistency of Pseudolabels for Cross-Domain Object Detection in Remote Sensing

Abstract

Domain shifts caused by varying remote sensing sensors severely degrade the model performance of object detection when deployed in target domains. While unsupervised domain adaptation provides a promising solution, existing methods tend to suffer from pseudolabel noise accumulation, leading to unstable and unreliable detection in complex remote sensing scenarios. To address both domain discrepancy and pseudolabel degradation, we propose ICPNet, an Iterative Consistency Pseudo-Labeling Network that integrates self-supervised learning with pseudolabel refinement to enhance domain robustness and label reliability. Specifically, we design an adversarial teacher-student framework enhanced with an Auxiliary Masked Autoencoder to learn domain-invariant representations by randomly masking feature regions and enforcing cross-domain reconstruction. In addition, we introduce an Iterative Consistency Pseudolabel Filter (ICPF) that leverages a memory of historical predictions. By filtering labels based on their stability across multiple training iterations, the ICPF generates a high-quality and stable supervisory information. A confidence decay mechanism further enhances stability by reducing the influence of outdated predictions. Extensive experiments on diverse remote sensing datasets demonstrate that our ICPNet outperforms state-of-the-art unsupervised domain adaptation (UDA) approaches in cross-domain object detection.