域意识的对抗性域增强网络用于高光谱图像分类
概括
本研究介绍了DADAnet,这是一个用于高光谱图像分类的新网络. 它有效地使用现有数据来分类新的场景,提高不同数据集的准确性.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 在各种场景中对超谱遥感图像进行分类是一个重大挑战.
- 为未见的样本 (目标域) 利用历史标记数据 (源域) 需要具有强大的概括能力的模型.
研究的目的:
- 提出一个新的单域泛化 (SDG) 网络,DADAnet,用于跨场景的高光谱图像分类 (HSIC).
- 提高在有限的源域数据上训练的模型的概括能力.
主要方法:
- DADAnet采用两阶段的方法:对抗域增强 (ADA) 和特定任务培训.
- ADA采用渐进式对抗生成策略,使用域意识空间光谱面罩 (DSSM) 编码器和两级对比损失 (TCC) 来创建多种增强域样本.
- 具体任务培训涉及对源数据和增强域数据的监督学习.
主要成果:
- DADAnet在三个不同的超光谱图像数据集上实现了高整体准确度的80.69%,63.75%和87.61%.
- 提出的方法优于现有的域调整 (DA) 和域泛化 (DG) 技术.
结论:
- 达达网在跨场景的高光谱图像分类方面表现出卓越的性能.
- 域意识对抗域增强策略有效地改善了对未见的目标域的模型概括.
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