STARS: Sensor-agnostic Transformer Architecture for Remote Sensing
This work addresses the challenge of sensor diversity in remote sensing for researchers and practitioners, though it appears incremental as it builds on existing transformer and foundation model concepts.
The paper tackles the problem of handling diverse spectral data from different sensors by introducing a sensor-agnostic transformer architecture with a Universal Spectral Representation, enabling a single model to ingest data from any sensor and generalize to unseen sensors effectively.
We present a sensor-agnostic spectral transformer as the basis for spectral foundation models. To that end, we introduce a Universal Spectral Representation (USR) that leverages sensor meta-data, such as sensing kernel specifications and sensing wavelengths, to encode spectra obtained from any spectral instrument into a common representation, such that a single model can ingest data from any sensor. Furthermore, we develop a methodology for pre-training such models in a self-supervised manner using a novel random sensor-augmentation and reconstruction pipeline to learn spectral features independent of the sensing paradigm. We demonstrate that our architecture can learn sensor independent spectral features that generalize effectively to sensors not seen during training. This work sets the stage for training foundation models that can both leverage and be effective for the growing diversity of spectral data.