通过三重关系感知度的三重关系感知度进行跨域的几次远程传感对象分类
概括
这项研究引入了一种新的三重关系感知度量,用于几次拍摄的遥感对象分类. 它通过调整实例间距离来增强特征表示,改善跨域概括.
科学领域:
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 收集特殊类别的遥感数据是昂贵的,技术要求高.
- 在不同的遥感数据集之间存在域分布差距.
- 现有的方法在自上而下的遥感图像中与微妙的类间差异作斗争.
研究的目的:
- 提出一种新的三重关系感知度量,用于跨领域的少数拍摄遥感对象分类.
- 通过精确调整实例间距离来增强特征表示.
- 为了实现域不变特征表示,提高分类准确性.
主要方法:
- 开发了一个三重关系感知度量,调整相同实例,相同类和不同的类关系.
- 使用相同的实例距离作为一个坐标起源的度量学习.
- 雇员关系意识的软标签和对抗性攻击来调整域名特征.
- 实施了插曲式的对抗性攻击,以生成伪源域名.
主要成果:
- 拟议的方法显著改善了特征表示学习.
- 在跨领域的几次射击遥感对象分类中实现了卓越的性能.
- 在五个广泛使用的遥感数据集中证明了有效性.
结论:
- 三重关系感知度量有效地解决了少数拍摄遥感分类中的挑战.
- 该方法为域不变特征学习提供了一个强大的方法.
- 在广泛的实验中超越了最先进的方法.
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