MAESTRO：面向多模态、多时相及多光谱地球观测数据的掩码自编码器

摘要

自监督学习在遥感领域展现出巨大潜力，但标准的自监督方法需针对地球观测数据的独特特性进行调整。我们在此方向上迈出一步，对多模态、多时相及多光谱地球观测数据的融合策略与重建目标归一化方案进行了全面基准测试。基于研究发现，我们提出了MAESTRO，一种基于掩码自编码器的新型改进方案，其特色在于优化了融合策略，并引入了一种定制化的目标归一化方案，该方案将光谱先验作为自监督信号。在四个地球观测数据集上的评估表明，MAESTRO在高度依赖多时相动态的任务中确立了新的技术标杆，同时在以单一单时相模态为主导的任务中仍保持强劲竞争力。重现我们所有实验的代码已发布于https://github.com/ignf/maestro。

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 reconstruction target normalization schemes for multimodal, multitemporal, and multispectral Earth observation data. Based on our findings, we propose MAESTRO, a novel adaptation of the Masked Autoencoder, featuring optimized fusion strategies and a tailored target normalization scheme that introduces a spectral prior as a self-supervisory signal. Evaluated on four Earth observation datasets, MAESTRO sets a new state-of-the-art on tasks that strongly rely on multitemporal dynamics, while remaining highly competitive on tasks dominated by a single mono-temporal modality. Code to reproduce all our experiments is available at https://github.com/ignf/maestro.

MAESTRO：面向多模态、多时相及多光谱地球观测数据的掩码自编码器

MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data

摘要

Support