FDGNet:频率解和数据几何学用于跨场景的高光谱图像分类领域的泛化
IEEE transactions on neural networks and learning systems
|August 26, 2024
概括
用于高光谱图像分类 (HSIC) 的域泛化得到了FDGNet的改进. 这种新的方法使用频率解和数据几何来概括到没有目标数据的未见域,提高分类准确性.
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:
- 跨场景的高光谱图像分类 (HSIC) 面临着由于域移位的挑战.
- 现有的域调整 (DA) 方法需要目标数据,这限制了实际应用.
- 当前的域泛化 (DG) 方法往往会损害语义信息或产生不切实际的样本.
研究的目的:
- 为跨场景的HSIC提出一个新的域泛化网络 (FDGNet).
- 解决现有总局方法的局限性,例如语义妥协和不切实际的样本生成.
- 提高高光谱图像分类模型对未见域的概括能力.
主要方法:
- 开发了一种带有频率脱的光谱空间编码器 (FDSS编码器),以在模拟域间差距时保持语义一致性.
- 将数据几何体纳入对抗性培训,以现实地多样化新领域.
- 提出了一个中间域采样策略,使用类智能感知多元组来合成可靠的中间域.
主要成果:
- 在跨场景的HSIC任务中,FDGNet表现出卓越的性能.
- 提出的方法有效地保持了语义一致性,并产生了现实的域变化.
- 阶级明智的感知多元策略增强了域不变表示的学习.
结论:
- 在HSIC中,FDGNet提供了一个强大的解决方案,用于在HSIC中进行域泛化.
- 频率解和数据几何学的集成是有效处理域间变化的.
- 该方法可以成功地将其推广到未见的领域,而无需在培训期间使用目标数据.
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