SCFormer:光谱坐标变压器用于跨域的几拍超光谱图像分类
概括
SCFormer通过集成光谱坐标来增强跨域高光谱图像分类 (HSIC). 这种方法提高了模型的稳定性和知识转移,在少数拍摄的学习场景.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 使用CNN或GCN的Few-shot学习 (FSL) 方法已经进行了先进的跨域高光谱图像分类 (HSIC).
- 现有的方法往往忽略了光谱坐标信息,限制了可解释性,稳定性和知识传输.
研究的目的:
- 提出一个有效的方法,用于跨领域的少数镜头高光谱图像分类 (CDFSL HSIC).
- 通过利用光谱坐标信息来增强模型的概括性和解释性.
主要方法:
- 引入了光谱坐标变压器 (SCFormer),一种不对称的编码器解码器架构.
- 集成光谱坐标 (SC) 块与旋转位置嵌入 (RoPE) 保存光谱坐标信息.
- 在SC块内开发了随机和顺序的面具模式,以实现高效的学习.
- 设计了一个域内损失函数,使用直角互补空间投射 (OCSP) 理论进行样本聚合和可解释性.
- 设计了一个使用瓦瑟斯坦距离 (WD) 进行域间对齐的域间损失函数.
主要成果:
- 在CDFSL HSIC的四个基准数据集上,SCFormer表现出卓越的性能.
- 提出的方法有效地减少了光谱位置干扰,并改善了特征表示概括.
- 不对称的架构和掩盖策略使高容量模型的有效学习成为可能.
- 基于OCSP的域内损失促进了域内一致性和可解释性.
- 基于WD的域间损失实现了有效的域对齐.
结论:
- 在跨域高光谱图像分类方面,SCFormer提供了显著的进步,特别是在少数镜头学习设置中.
- 整合光谱坐标信息和新的损失函数可以提高模型的稳定性,可解释性和概括性.
- 拟议的架构为应对超光谱成像领域转移的挑战提供了一个有效的框架.
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