构建通用光谱表征，让模型跨传感器识别光谱数据。
STARS: Sensor-agnostic Transformer Architecture for Remote Sensing
- 用传感器元数据统一不同设备的光谱数据
- 自监督预训练使模型不依赖特定传感器
- 适合处理多源光谱数据的研究者
我们提出一种传感器无关的光谱变换器架构，作为光谱基础模型的基础。为此，引入通用光谱表征（USR），利用传感核规格和波长等传感器元数据，将任意光谱仪器获取的光谱数据编码为统一表示，使单一模型可处理任何传感器的数据。此外，我们设计了一种新型随机传感器增强与重建的自监督预训练方法，以学习与传感范式无关的光谱特征。实验表明，该架构能有效学习传感器无关的光谱特征，并泛化至训练中未见的传感器。本工作为训练可适应日益多样化光谱数据的基础模型奠定了基础。
原文摘要 · Abstract (English)
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.
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