用变分自编码器压缩高光谱数据，实现514倍压缩率并保留关键大气信息。
Hyperspectral Variational Autoencoders for Joint Data Compression and Component Extraction
- 设计高光谱专用VAE，实现514倍压缩率
- 压缩后重建误差低于信号1-2个数量级
- 可提取云量和臭氧等大气成分，但痕量气体提取仍具挑战
地球同步高光谱卫星每日产生数TB数据，带来存储、传输与分发的严峻挑战。本文提出一种变分自编码器（VAE）方法，对NASA TEMPO卫星高光谱观测数据（1028个波段，290-490nm）实现514倍压缩，重建误差在所有波长上均比信号低1-2个数量级。该压缩大幅降低数据量，支持高效归档与共享，同时保持光谱保真度。除压缩外，我们评估压缩后的隐空间能否保留大气信息，通过线性和非线性探针提取二级产品（NO2、O3、HCHO、云量）。云量和总臭氧提取性能良好（R²分别为0.93和0.81），因其光谱特征明显；而对流层痕量气体提取困难（NO2 R²=0.20，HCHO R²=0.51），反映其信号弱且大气相互作用复杂。关键发现：VAE以半线性方式编码大气信息——非线性探针显著优于线性探针，且训练时引入显式潜变量监督提升有限，揭示部分产品的编码本质挑战。本工作证明神经压缩可大幅降低高光谱数据体积，同时保留关键大气信号，解决下一代地球观测系统的关键瓶颈。
原文摘要 · Abstract (English)
Geostationary hyperspectral satellites generate terabytes of data daily, creating critical challenges for storage, transmission, and distribution to the scientific community. We present a variational autoencoder (VAE) approach that achieves x514 compression of NASA's TEMPO satellite hyperspectral observations (1028 channels, 290-490nm) with reconstruction errors 1-2 orders of magnitude below the signal across all wavelengths. This dramatic data volume reduction enables efficient archival and sharing of satellite observations while preserving spectral fidelity. Beyond compression, we investigate to what extent atmospheric information is retained in the compressed latent space by training linear and nonlinear probes to extract Level-2 products (NO2, O3, HCHO, cloud fraction). Cloud fraction and total ozone achieve strong extraction performance (R^2 = 0.93 and 0.81 respectively), though these represent relatively straightforward retrievals given their distinct spectral signatures. In contrast, tropospheric trace gases pose genuine challenges for extraction (NO2 R^2 = 0.20, HCHO R^2 = 0.51) reflecting their weaker signals and complex atmospheric interactions. Critically, we find the VAE encodes atmospheric information in a semi-linear manner - nonlinear probes substantially outperform linear ones - and that explicit latent supervision during training provides minimal improvement, revealing fundamental encoding challenges for certain products. This work demonstrates that neural compression can dramatically reduce hyperspectral data volumes while preserving key atmospheric signals, addressing a critical bottleneck for next-generation Earth observation systems. Code - https://github.com/cfpark00/Hyperspectral-VAE
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