高光谱数据终于不再被孤立——SpectralEarth-FM 让遥感团队能用上更全面的传感器信息，做土地覆盖分类或环境监测的开发者可以直接参考其开源架构和数据集。
地球观测基础模型通常基于多光谱、SAR等多传感器数据训练，但高光谱影像（HSI）一直未被充分整合。SpectralEarth-FM 提出一种层次化Transformer架构，通过光谱标记化、传感器专用编码器和跨传感器融合模块，实现HSI与低通道观测数据的联合处理。研究团队构建了包含EnMAP、EMIT、DESIS等星载HSI数据与Sentinel-2、Landsat-8/9、Sentinel-1等数据的SpectralEarth-MM数据集，覆盖约200万个全球位置、2500万地理参考图块，数据量超40TB。模型采用JEPA风格预训练目标，在HSI下游任务和标准地球观测基准上均达到最先进水平。这项工作填补了高光谱与多模态遥感联合预训练的空白，为环境监测、农业、地质勘探等领域提供了更丰富的数据基础。
SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining
Earth observation (EO) foundation models (FMs) are increasingly trained on multisensor data, spanning multispectral imagery (MSI), synthetic aperture radar (SAR), and derived geospatial layers, but hyperspectral imagery (HSI) remains underrepresented. Conversely, existing hyperspectral FMs are trained on HSI alone, leaving joint pretraining and fusion of HSI with co-located EO sensors unexplored. We introduce SpectralEarth-FM, a hierarchical transformer for multisensor EO input with heterogeneous spectral dimensionality. The architecture combines spectral tokenization for hyperspectral inputs, sensor-specific encoders, a cross-sensor fusion module, and a shared hierarchical encoder, enabling joint processing of HSI and lower-channel observations. To pretrain SpectralEarth-FM, we curate SpectralEarth-MM, a dataset that co-locates HSI from three spaceborne sensors (EnMAP, EMIT, DESIS) with Sentinel-2, Landsat-8/9 optical imagery, Landsat land surface temperature (LST), and Sentinel-1 SAR, over common geographic footprints. It comprises approximately 2M globally distributed locations, 25M georeferenced patches, and over 40TB of data. Pretraining uses a Joint-Embedding Predictive Architecture (JEPA)-style objective that matches representations between global views and single-sensor local views from the same location. We evaluate SpectralEarth-FM on hyperspectral downstream tasks and standard EO benchmarks following the PANGAEA protocol, achieving state-of-the-art results across both evaluation settings.