  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.
