A Latent Representation Learning Framework for Hyperspectral Image Emulation in Remote Sensing

• URL: http://arxiv.org/abs/2603.21911v1
• Date: Mon, 23 Mar 2026 12:32:09 GMT
• Title: A Latent Representation Learning Framework for Hyperspectral Image Emulation in Remote Sensing
• Abstract summary: We propose a latent representation-based framework for hyperspectral emulation that learns a latent generative representation of hyperspectral data.<n>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.<n> Experiments on PROSAIL-simulated vegetation data and Sentinel-3 OLCI imagery demonstrate that the method outperforms classical regression-based emulators.
• Score: 2.607903424600012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 often limited to spectrum-level outputs. In this work, we propose a latent representation-based framework for hyperspectral emulation that learns a latent generative 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 interpolation. 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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