@inproceedings{3be94f59124748d28534c01be31e3f91,
title = "Multi-Aspect Feature Enhancement Network for Aircraft Detection in High-Resolution SAR Images",
abstract = "Aircraft detection using Synthetic Aperture Radar (SAR) images plays a crucial role in transportation and military applications. Nevertheless, the unique imaging characteristics of SAR often render aircraft targets as discrete points, and their complex geometric structures vary under different imaging conditions. Moreover, the complex background, especially with strong-scattering elements like buildings, significantly complicates detection. To overcome these challenges, this paper introduces a multi-aspect feature enhancement network called MAFEN for aircraft detection in high-resolution SAR images. MAFEN combines a Multi-scale Feature Enhancement Module (MSFEM) with a Key Structure Enhancement Module (KSEM), thereby enhancing detection accuracy and efficiency. Experimental results on public datasets demonstrate significant improvements in detecting aircraft targets in complex scenes.",
keywords = "Aircraft detection, Deep Learning, Feature enhancement, SAR",
author = "Yu Qiu and Bin Zou and Jiang Qin and Lamei Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 ; Conference date: 07-07-2024 Through 12-07-2024",
year = "2024",
doi = "10.1109/IGARSS53475.2024.10642268",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7777--7781",
booktitle = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "美国",
}