用潜在表示学习生成高光谱图像，提升真实感与下游应用性能
A Latent Representation Learning Framework for Hyperspectral Image Emulation in Remote Sensing
- 基于概率潜空间建模高光谱数据，支持光谱与空间联合生成
- 在植被与哨兵3数据上重建精度和光谱保真度优于传统方法
- 适合遥感算法开发、任务设计及生物物理参数反演应用
高光谱图像合成对大规模仿真、算法开发和任务设计至关重要，但传统辐射传输模型计算成本高，现有模拟方法多仅限于光谱级输出。本文提出一种基于潜在表示的高光谱模拟框架，学习高光谱数据的概率潜空间表示。该方法支持光谱级与空间-光谱联合模拟，可采用单步直接训练或两步策略（先训练变分自编码器，再建立参数到潜变量映射）。在PROSAIL模拟植被数据与哨兵3 OLCI影像上的实验表明，该方法在重建精度、光谱保真度及对真实空间变异的鲁棒性方面均优于经典回归型模拟器。进一步验证显示，生成的高光谱图像在下游生物物理参数反演中保持了良好性能，凸显其在遥感应用中的实用价值。
原文摘要 · Abstract (English)
Synthetic hyperspectral image (HSI) generation is essential for large-scale simulation, algorithm development, and mission design, yet traditional radiative transfer models remain computationally expensive and proposed emulation methods are often limited to spectrum-level outputs. In this work, we propose a latent representation-based framework for hyperspectral emulation that learns a probabilistic latent representation of hyperspectral data. The proposed approach supports both spectrum-level and spatial-spectral emulation and can be trained either in a direct one-step formulation or in a two-step strategy that couples variational autoencoder (VAE) pretraining with parameter-to-latent mapping. Experiments on PROSAIL-simulated vegetation data and Sentinel-3 OLCI imagery demonstrate that the method outperforms classical regression-based emulators in reconstruction accuracy, spectral fidelity, and robustness to real-world spatial variability. We further show that emulated HSIs preserve performance in downstream biophysical parameter retrieval, highlighting the practical relevance of emulated data for remote sensing applications.
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