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联盟:全合一的光谱空间频率意识基础模型
IEEE transactions on pattern analysis and machine intelligence
|December 3, 2025
概括
本研究介绍了用于遥感的全合一光谱空间频率意识基础模型 (联盟). 联盟有效地整合了光谱,空间和频率信息,显著提高了基础模型在各种任务上的性能.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 遥感中的基础模型 (FMs) 在空间和光谱数据方面表现出色,但未充分利用频域信息.
- 现有的FM很难弥合频率特征和原始图像内容之间的语义差距,阻碍下游任务执行.
研究的目的:
- 开发一个新的基础模型,联盟,全面整合光谱,空间和频率信息,以进行增强的遥感分析.
- 解决当前FM在捕获频域内隐藏特征并保持多域连贯性方面的局限性.
主要方法:
- 提出了全合一的光谱空间频率意识基础模型 (联盟) 框架.
- 引入了一种渐进的频率解码机制,模仿人类的视觉认知,以尽量减少多域信息差距.
- 开发了一种三域融合注意模块,用于集成处理振幅,相位和光谱空间关系.
- 实现了频率嵌入与专门的令牌初始化,用于细粒度频段建模.
主要成果:
- 联盟在6个下游遥感任务中表现出卓越的性能.
- 渐进频率解码有效地提取难以在原始像素值中观察到的微妙图像模式.
- 三重域融合注意力和频率嵌入使全面的特征集成和细粒度建模成为可能.
结论:
- 联盟代表了远程传感基础模型的重大进步,通过有效利用频率域.
- 提出的方法成功地弥合了不同数据领域之间的语义差距,从而提高了概括性和性能.
- 黄河数据集为评估FM能力提供了强大的基准,用于挑战跨领域遥感场景.
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