用多光谱数据生成高光谱图像，提升温室气体监测精度。
Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space
- 通过自监督预训练的光谱变换器，从多光谱输入合成高光谱数据。
- 合成数据在时空覆盖上优于传统高光谱，温室气体预测准确率显著提升。
- 适合关注大气监测、遥感图像重建的研究者与应用开发者。
高光谱成像能提供详细的光谱信息，在温室气体（GHGs）监测中潜力巨大，但受限于空间覆盖范围小和重访时间间隔长。相比之下，多光谱成像虽具有更广的空间和时间覆盖，却常缺乏增强温室气体探测能力的光谱细节。为解决此问题，本研究提出一种光谱变换器模型，可从多光谱输入合成高光谱数据。模型通过波段级掩码自编码器进行预训练，并在时空对齐的多光谱-高光谱图像对上微调。生成的合成高光谱数据保留了多光谱影像的时空优势，且相比仅使用多光谱数据，显著提升了温室气体预测精度。该方法有效缓解了光谱分辨率与覆盖范围之间的权衡，展示了结合高光谱与多光谱系统优势、利用自监督深度学习推动大气监测发展的潜力。
原文摘要 · Abstract (English)
Hyperspectral imaging provides detailed spectral information and holds significant potential for monitoring of greenhouse gases (GHGs). However, its application is constrained by limited spatial coverage and infrequent revisit times. In contrast, multispectral imaging offers broader spatial and temporal coverage but often lacks the spectral detail that can enhance GHG detection. To address these challenges, this study proposes a spectral transformer model that synthesizes hyperspectral data from multispectral inputs. The model is pre-trained via a band-wise masked autoencoder and subsequently fine-tuned on spatio-temporally aligned multispectral-hyperspectral image pairs. The resulting synthetic hyperspectral data retain the spatial and temporal benefits of multispectral imagery and improve GHG prediction accuracy relative to using multispectral data alone. This approach effectively bridges the trade-off between spectral resolution and coverage, highlighting its potential to advance atmospheric monitoring by combining the strengths of hyperspectral and multispectral systems with self-supervised deep learning.
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