[2603.21911] A Latent Representation Learning Framework for Hyperspectral Image Emulation in Remote Sensing

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.21911 (cs) [Submitted on 23 Mar 2026] Title:A Latent Representation Learning Framework for Hyperspectral Image Emulation in Remote Sensing Authors:Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel, Matthieu Puigt View a PDF of the paper titled A Latent Representation Learning Framework for Hyperspectral Image Emulation in Remote Sensing, by Chedly Ben Azizi and 3 other authors View PDF HTML (experimental) 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 biophysica...