Mamba-UDA: Mamba Unsupervised Domain Adaptation for SAR Ship Detection

Abstract

The existing synthetic aperture radar (SAR) ship detectors perform well on data with consistent distributions but degrade significantly when faced with domain shifts and the absence of labeled data. Moreover, the traditional convolutional neural networks (CNNs) struggle with global feature extraction due to the local receptive fields, while transformer approaches struggle with computational efficiency when extracting global features from complex SAR images. Designing an effective cross-domain SAR ship detector that can handle unlabeled data with domain shifts remains a challenge. In this letter, we propose a novel Mamba-based unsupervised domain adaptation (UDA) SAR ship detection model integrated with pseudolabels optimization strategy. First, we propose the domain adaptive state-space module (DASSM) to construct the Mamba mean teacher (MMT) framework for the first time, enhancing the capture of both global and local SAR image features at a linear time complexity and facilitating domain-invariant feature learning. To enhance the quality of pseudolabels, we design the adaptive pseudolabel optimizer (APLO) module with wise-IoU (WIoU) and dynamic dual-threshold pseudolabel selector (DDPLS). The WIoU is utilized to improve the generation of pseudolabels, while DDPLS is further employed to categorize and optimize pseudolabels. Extensive experiments on public datasets illustrate the effectiveness and superiority of the proposed method for cross-domain detection of unlabeled SAR data.