Cross-scene target detection based on feature adaptation and uncertainty-aware pseudo-label learning for high resolution SAR images

Abstract

The characteristics of synthetic aperture radar (SAR) images are easily affected by factors such as sensor parameters, imaging scenes, etc., which may lead to data distributional discrepancies between the ideal training phase and the practical application phase, resulting in generalization performance degradation of deep learning based detectors in practice. In this paper, an accurate and fast domain adaptive detection method is proposed to address the performance degradation problem of cross-scene detection without requiring additional manual annotations. The cross-scene detection performance is gradually improved by confidence-induced unsupervised feature adaptation (CUFA) and uncertainty-aware adaptive pseudo-label learning (UAPL). In CUFA, image-level and instance-level feature adaptation are progressively implemented to bridge the distribution discrepancies between the source and the target domain in the latent feature space. Then, UAPL is performed to adaptively introduce accurate and stable supervisions of target domain according to the learning status of the detector, avoiding error accumulations and further improving the cross-scene target detection performance. The proposed cross-scene detection method is validated on airborne and spaceborne SAR image datasets of different imaging scenes. The experimental results show that by combining both CUFA and UAPL, compared with the second-best comparison method, the proposed method significantly improves the cross-scene target detection performance in SAR images (at least + 6.5% AP50 and F1 in miniSAR and FARAD cross-scene tasks, and at least + 7.5% AP50 and F1 in Gaofen-3 and Sentinel-1 cross-scene tasks), which facilitates application of the deep learning based detectors in the realistic scenarios.