SpecTf: Transformers enable data-driven imaging spectroscopy cloud detection

• 成果类型: Article
• 署名作者: Lee, Jake H.; Kiper, Michael; Thompson, David R.; Brodrick, Philip G.
• 署名单位: National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); California Institute of Technology
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13796
• DOI: 10.1073/pnas.2502903122
• 发表日期: 2025-07-08
• 关键词: aircraft; channels; SHADOW

摘要

Current and upcoming generations of visible-shortwave infrared (VSWIR) imaging spectrometers promise unprecedented capacity to quantify Earth system processes across the globe. However, reliable cloud screening remains a fundamental challenge for these instruments, where traditional spatial and temporal approaches are limited by cloud variability and limited temporal coverage. The Spectroscopic Transformer (SpecTf) addresses these challenges with a spectroscopy-specific deep-learning architecture that performs cloud detection using only spectral information (no spatial or temporal data are required). By treating spectral measurements as sequences rather than image channels, SpecTf learns fundamental physical relationships without relying on spatial context. Our experiments demonstrate that SpecTf significantly outperforms the current baseline approach implemented for the Earth surface Mineral dust source InvesTigation (EMIT) instrument, and performs comparably with other machine learning methods with orders of magnitude fewer learned parameters. Critically, we demonstrate SpecTf's inherent interpretability through its attention mechanism, revealing physically meaningful spectral features the model has learned. Finally, we present SpecTf's potential for cross-instrument generalization by applying it to different instrument on a different platform without modifications, opening the door to instrument-agnostic data-driven algorithms for future imaging spectroscopy tasks.