原因HSI:通过因果解来进行超谱图像分类的反事实增强域泛化
Xin Li1, Zongchi Yang1, Wenlong Li1
1College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China.
Journal of imaging
|February 26, 2026
概括
原因HSI通过使用因果关系来改善跨场景高光谱图像 (HSI) 的分类. 这一框架解决了领域转移和虚假的相关性,增强了对新的遥感场景的概括性.
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
- 因果推理因果推理
背景情况:
- 跨场景高光谱图像 (HSI) 的分类是具有挑战性的,因为源和目标场景之间的域移动.
- 现有的单源域泛化 (DG) 方法难以适应看不见的遥感环境.
- 因果论为理解HSI数据中的分布转移和混偏差提供了一个框架.
研究的目的:
- 开发一个因果关系启发的框架 (CauseHSI) 进行强大的跨场景的HSI分类.
- 为了解决干预分布转移和混偏差在HSI领域的概括.
- 提高HSI分类模型对未见的目标场景的概括能力.
主要方法:
- 提出了CauseHSI,这是一个框架,将因果论整合到HSI领域概括中.
- 实施了反事实生成模块 (CGM) 来模拟跨领域干预并生成多种变体.
- 开发了一种因果解模块 (CDM),以使用结构因果模型将不变因果特征与虚假相关性分开.
主要成果:
- 在保持语义一致性的同时,CauseHSI有效模拟跨领域干预.
- 该CDM成功地将域不变的因果语义从域特定的虚假相关关系中分离出来.
- 在Pavia,Houston和HyRANK数据集上,CauseHSI表现出优于现有的GD方法的卓越性能.
结论:
- 将模型学习与因果原理对齐,可以提高对HSI分类领域转移的稳定性.
- 原因HSI提供了一种新的方法来解决跨场景HSI领域概括的基本障碍.
- 拟议的框架显著提高了用于遥感应用的HSI分类模型的通用性.
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