Active Style-Content Dual-Branch Domain Adaptation for Semi-Supervised SAR Object Detection

Area of Science:

• Remote Sensing
• Computer Vision
• Machine Learning

Background:

• Synthetic Aperture Radar (SAR) imaging provides all-weather, all-day remote sensing capabilities.
• High costs and annotation time limit SAR image implementation.
• Semi-supervised domain adaptation (SSDA) uses optical and limited SAR data for SAR object detection.

Purpose of the Study:

• To address inefficient random sampling in existing SSDA object detection methods for SAR images.
• To bridge the significant style and content gap between optical and SAR imagery.
• To propose an active style-content dual-branch domain adaptation method for semi-supervised object detection in SAR images.

Main Methods:

• Task-aware Active Sampling (TAS) module for selecting valuable SAR samples.
• Dual-branch framework to handle optical-SAR image discrepancies.
• Multi-layer Feature Alignment (MFA) for style consistency.
• Gaussian-SAM Image Fusion (G-SIF) for content integration.

Main Results:

• The proposed method significantly improves object detection performance on SAR images.
• Active sampling enhances the exploitation of target domain data features.
• The dual-branch approach effectively aligns style and content between optical and SAR domains.
• Experiments show exceptional generalization capabilities on ship and aircraft datasets.

Conclusions:

• The active style-content dual-branch domain adaptation method is effective for semi-supervised object detection in SAR images.
• The TAS, MFA, and G-SIF modules successfully address key challenges in SAR image analysis.
• The approach offers a more efficient and accurate solution for SAR remote sensing applications.