Mixture-of-Experts in Remote Sensing: A Survey

Abstract

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.

Figures (9)

• Figure 1: Word cloud of the most frequent words appearing in MoE-related remote sensing papers.
• Figure 2: Basic architecture of Mixture-of-Experts (MoE).
• Figure 3: Overview of Mixture-of-Experts applications in remote sensing.
• Figure 4: MoE in adaptive mixture-of-experts distillation (AMoED) fu2025adaptive for cross-satellite generalizable incremental scene classification.
• Figure 5: MoE in mixture-of-spectral-spatial-experts state space model (MambaMoE) xu2025mambamoe for hyperspectral image classification.