基于标签噪声学习的SAR目标分类方法
Hongqiang Wang1, Yuqing Lan2, Fuzhan Yue3
1School of Software, Beihang University, Beijing, 100191, China; Jiangxi Research Institute, Beihang University, Beijing, 100191, China; State Key Laboratory of Space-Earth Integrated Information Technology, Beijing Institute of Satellite Information Engineering, Beijing, 100095, China.
概括
这项研究引入了一种新的方法,通过减少特征和标签中的噪声来改善合成孔径雷达 (SAR) 目标识别. 这种方法提高了卷积神经网络 (CNN) 的性能,即使数据有很大的不准确性.
科学领域:
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
- 信号处理 信号处理
背景情况:
- 合成孔径雷达 (SAR) 目标识别对于图像解释至关重要.
- 卷积神经网络 (CNN) 擅长SAR图像分类,但需要大型标记数据集.
- SAR数据容易出现和标记噪声,降低了CNN的性能.
研究的目的:
- 开发一种可靠的SAR目标分类方法,以减轻特征和标签噪声.
- 在数据不准确的情况下,提高基于CNN的分类器的性能.
主要方法:
- 建议采用基于 L_p 规范化的动态散射特征提取方法来处理特征噪声.
- 开发了一个强大的表示学习框架,以提高对标签噪声的模型稳定性.
- 神经网络被用来调整规范化参数,并尽量减少样本和类原型之间的距离.
主要成果:
- 拟议的方法在MSTAR,SAR-ACD和FUSAR数据集上显示了强大的分类准确性.
- 标签噪声水平在0%至60%之间实现了一致的性能.
- 由于注释不准确而导致的不良影响有显著的减轻.
结论:
- 开发的方法有效地解决了SAR目标分类中的特征和标签噪声.
- 这种方法在基于CNN的SAR图像解释中提供了显著的改进,特别是在杂的数据中.
- 这些发现有助于更可靠的SAR目标识别系统.
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