A General Purpose Spectral Foundational Model for Both Proximal and Remote Sensing Spectral Imaging
This addresses the challenge of building predictive models for spectral imaging with limited data, offering a general-purpose solution for researchers and practitioners in fields like remote sensing and proximal imaging.
The paper tackles the problem of limited spectral imaging datasets by proposing a large-scale foundational model and dataset based on masked autoencoders, achieving adaptability and robustness for downstream tasks across both proximal and remote sensing with hundreds of channels.
Spectral imaging data acquired via multispectral and hyperspectral cameras can have hundreds of channels, where each channel records the reflectance at a specific wavelength and bandwidth. Time and resource constraints limit our ability to collect large spectral datasets, making it difficult to build and train predictive models from scratch. In the RGB domain, we can often alleviate some of the limitations of smaller datasets by using pretrained foundational models as a starting point. However, most existing foundation models are pretrained on large datasets of 3-channel RGB images, severely limiting their effectiveness when used with spectral imaging data. The few spectral foundation models that do exist usually have one of two limitations: (1) they are built and trained only on remote sensing data limiting their application in proximal spectral imaging, (2) they utilize the more widely available multispectral imaging datasets with less than 15 channels restricting their use with hundred-channel hyperspectral images. To alleviate these issues, we propose a large-scale foundational model and dataset built upon the masked autoencoder architecture that takes advantage of spectral channel encoding, spatial-spectral masking and ImageNet pretraining for an adaptable and robust model for downstream spectral imaging tasks.