针对遥感数据特点，提出新型自监督模型MAESTRO，提升多模态时空数据建模能力。
MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data
- 设计融合机制与光谱先验归一化，优化多源遥感数据重建
- 在四个数据集上实现时序动态建模的领先性能，跨数据集也表现稳定
- 适合需要处理多时相、多光谱遥感数据的研究者使用
自监督学习在遥感领域前景广阔，但标准方法需适配地球观测数据的独特特性。本文通过全面评估多模态、多时相、多光谱遥感数据的重建目标融合策略与归一化方案，发现关键影响因素。基于此，提出MAESTRO：一种改进的掩码自编码器，引入优化的融合机制与结合光谱先验的归一化方案作为自监督信号。在四个地球观测数据集上，无论同数据集还是跨数据集设置，MAESTRO在高度依赖时序动态的任务中均达到当前最优性能，同时在其他任务上保持竞争力。所有实验代码已公开于https://github.com/ignf/maestro。
原文摘要 · Abstract (English)
Self-supervised learning holds great promise for remote sensing, but standard self-supervised methods must be adapted to the unique characteristics of Earth observation data. We take a step in this direction by conducting a comprehensive benchmark of fusion strategies and normalization schemes of reconstruction targets for multimodal, multitemporal, and multispectral Earth observation data. Based on our findings, we introduce MAESTRO, a novel adaptation of the Masked Autoencoder with optimized fusion mechanisms and a normalization scheme that incorporates a spectral prior as a self-supervisory signal. Evaluated on four Earth observation datasets in both intra- and cross-dataset settings, MAESTRO achieves state-of-the-art performance on tasks that strongly rely on multitemporal dynamics, while also remaining competitive on others. Code to reproduce all our experiments is available at https://github.com/ignf/maestro.
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