Year: 2025
Type: article
Abstract: SAR target detection and signal detection are critical tasks in electromagnetic signal processing, with wide-ranging applications in remote sensing and communication monitoring. However, these tasks are challenged by complex backgrounds, multi-scale target variations, and the limited integration of domain-specific priors into existing deep learning models. To address these challenges, we propose Refined Deformable-DETR, a novel Transformer-based method designed to enhance detection performance in SAR and signal processing scenarios. Our approach integrates three key components, including the half-window filter (HWF) to leverage SAR and signal priors, the multi-scale adapter to ensure robust multi-level feature representation, and auxiliary feature extractors to enhance feature learning. Together, these innovations significantly enhance detection precision and robustness. The Refined Deformable-DETR achieves a mAP of 0.682 on the HRSID dataset and 0.540 on the spectrograms dataset, demonstrating remarkable performance compared to other methods.
Source: Remote Sensing
DOI: 10.3390/rs17081406
Authors: Zhenghao Li, Xin Zhou
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4409479778