首篇系统综述遥感中专家混合模型的应用与前景
Mixture-of-Experts in Remote Sensing: A Survey
- 梳理遥感中MoE模型的原理与架构设计
- 覆盖多任务应用，揭示动态路由优势
- 适合遥感、AI交叉领域研究者参考
遥感数据分析与解读因传感器模态多样性和地球观测数据的时空动态性而面临独特挑战。专家混合（Mixture-of-Experts, MoE）模型作为一种强大范式，通过将输入动态路由至针对不同任务方面设计的专用专家，有效应对这些挑战。尽管进展迅速，该领域仍缺乏对MoE在遥感中应用的全面综述。本文首次系统性地概述了MoE在遥感中的应用，涵盖基础原理、架构设计及在多种遥感任务中的关键应用。同时，论文展望未来趋势，以激发更多关于将MoE应用于遥感的研究与创新。
原文摘要 · Abstract (English)
Remote sensing data analysis and interpretation present unique challenges due to the diversity in sensor modalities and spatiotemporal dynamics of Earth observation data. Mixture-of-Experts (MoE) model has emerged as a powerful paradigm that addresses these challenges by dynamically routing inputs to specialized experts designed for different aspects of a task. However, despite rapid progress, the community still lacks a comprehensive review of MoE for remote sensing. This survey provides the first systematic overview of MoE applications in remote sensing, covering fundamental principles, architectural designs, and key applications across a variety of remote sensing tasks. The survey also outlines future trends to inspire further research and innovation in applying MoE to remote sensing.
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