FCCD-SAR:一个基于FasterNet的轻量级SAR ATR算法
Xiang Dong1, Dong Li1, Jiandong Fang1
1The College of Information Engineering, Information and Communication Engineering, Inner Mongol University of Technology, Hohhot 010080, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了FCCD-SAR,这是一种用于合成光圈雷达 (SAR) 目标识别的新算法. 它通过使用轻量级网络实现了高精度和速度,改善了远程传感能力.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 对于合成光圈雷达 (SAR) 目标识别的深度学习模型通常优先考虑网络复杂性而不是效率.
- 现有的模型忽视了SAR图像分析中精度和计算速度之间的关键平衡.
研究的目的:
- 开发一个轻量级和高效的SAR目标识别算法,FCCD-SAR,平衡准确性和速度.
- 增强特征提取和散射信息检索,以改进SAR目标识别.
主要方法:
- 开发了一个与FasterNet架构集成的轻量级SAR特征提取骨干.
- 整合了CARAFE上抽样操作员,以增强散射信息提取.
- 使用C3-Faster模块来提高识别精度和计算效率.
- 实现了一个基于注意力的DyHead检测头来处理多级SAR目标变化.
主要成果:
- 在MSTAR数据集上,FCCD-SAR算法实现了99.5%的平均精度 (mAP).
- 该模型表现出高效率,只有272万个参数和6.11G FLOP.
- 提出的方法显著提高了SAR目标识别性能.
结论:
- FCCD-SAR为SAR目标识别提供了一种熟练且计算效率高的解决方案.
- 该算法的设计有效地解决了SAR图像中的规模变化和数据复杂性的挑战.
- 这项工作推动了深度学习在遥感中用于目标识别的应用.
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