遥感中的专家混合模型：综述
Mixture-of-Experts in Remote Sensing: A Survey
AI总结：
本文综述了专家混合模型在遥感中的应用，涵盖基本原理、架构设计及多种遥感任务中的关键应用，并展望了未来研究方向。
摘要：
遥感数据的分析和解释因传感器模态的多样性及地球观测数据的时空动态性而面临独特挑战。专家混合（MoE）模型通过动态路由输入到专门处理任务不同方面的专家，成为解决这些挑战的强大范式。然而，尽管进展迅速，社区仍缺乏对MoE在遥感中的全面回顾。本文提供了MoE在遥感中的系统综述，涵盖基本原理、架构设计及多种遥感任务中的关键应用。本文还概述了未来趋势，以激发进一步将MoE应用于遥感的研究和创新。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
