HyVIC: A Metric-Driven Spatio-Spectral Hyperspectral Image Compression Architecture Based on Variational Autoencoders

TL;DR

HyVIC is a novel spatio-spectral variational hyperspectral image compression architecture that explicitly balances spatial and spectral features, achieving state-of-the-art reconstruction fidelity and providing practical guidelines for future research.

Contribution

The paper introduces HyVIC, a new architecture with a metric-driven hyperparameter selection strategy tailored for hyperspectral data's unique redundancies.

Findings

Achieves up to 4.66dB improvement in BD-PSNR over previous methods.

Effectively balances spatial and spectral feature learning for high fidelity reconstruction.

Demonstrates robustness across various compression ratios on benchmark datasets.

Abstract

The rapid growth of hyperspectral data archives in remote sensing (RS) necessitates effective compression methods for storage and transmission. Recent advances in learning-based hyperspectral image (HSI) compression have significantly enhanced both reconstruction fidelity and compression efficiency. However, existing methods typically adapt variational image compression models designed for natural images, without adequately accounting for the distinct spatio-spectral redundancies inherent in HSIs. In particular, they lack explicit architectural designs to balance spatial and spectral feature learning, limiting their ability to effectively leverage the unique characteristics of hyperspectral data. To address this issue, we introduce spatio-spectral variational hyperspectral image compression architecture (HyVIC). The proposed model comprises four main components: 1) adjustable spatio-spectral encoder; 2) spatio-spectral hyperencoder; 3) spatio-spectral hyperdecoder; and 4) adjustable spatio-spectral decoder. We demonstrate that the trade-off between spatial and spectral feature learning is crucial for the reconstruction fidelity, and therefore present a metric-driven strategy to systematically select the hyperparameters of the proposed model. Extensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed model, achieving high spatial and spectral reconstruction fidelity across a wide range of compression ratios (CRs) and improving the state of the art by up to 4.66dB in terms of BD-PSNR. Based on our results, we offer insights and derive practical guidelines to guide future research directions in learning-based variational HSI compression. Our code and pre-trained model weights are publicly available at https://git.tu-berlin.de/rsim/hyvic .