The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.

翻译：基础模型因以自监督方式革新视觉表征学习领域的潜力而受到广泛关注。尽管大多数基础模型针对RGB图像进行定制以处理各类视觉任务，但在光谱数据（尤其遥感应用中为场景理解提供宝贵信息的数据）方面的研究仍存在明显空白。为填补这一空白，我们首次创建了通用遥感基础模型——SpectralGPT，该模型通过新型三维生成式预训练变压器（GPT）专门用于处理光谱遥感图像。与现有基础模型相比，SpectralGPT：1）采用渐进式训练方式，可处理不同尺寸、分辨率、时序和区域的输入图像，实现海量遥感大数据的充分利用；2）利用三维token生成实现空间-光谱耦合；3）通过多目标重建捕获光谱序列模式；4）在百万量级光谱遥感图像上完成训练，生成参数超过6亿的模型。实验评估表明，预训练的SpectralGPT模型在四项下游任务（单/多标签场景分类、语义分割和变化检测）中均展现出显著性能提升，标志着在地球科学领域推进光谱遥感大数据应用具有重大潜力。