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 this https URL .
Abstract (translated)
高光谱遥感（RS）数据档案的快速增长对存储和传输提出了有效的压缩方法需求。近年来基于学习的高光谱图像（HSI）压缩技术显著提升了重建精度与压缩效率。然而现有方法通常直接适配为自然图像设计的变分图像压缩模型，未能充分考量高光谱图像固有的独特空-谱冗余特性。具体而言，这些方法缺乏显式的架构设计来平衡空间与光谱特征学习，从而限制了其有效利用高光谱数据独特特性的能力。针对这一问题，本文提出空-谱变分高光谱图像压缩架构（HyVIC）。该模型包含四个核心组件：1）可调空-谱编码器；2）空-谱超编码器；3）空-谱超解码器；4）可调空-谱解码器。研究表明空间与光谱特征学习的权衡对重建精度至关重要，因此我们提出一种指标驱动策略来系统选择模型超参数。在两个基准数据集上的大量实验验证了所提模型的有效性，其在宽泛的压缩比（CR）范围内均能实现高空间与光谱重建精度，并以最高4.66dB的BD-PSNR值提升现有技术水平。基于研究成果，我们提供了见解并提炼出实用指南，以指导未来基于学习的变分HSI压缩研究方向。本文代码与预训练模型权重已公开于此https链接。
URL
https://arxiv.org/abs/2603.26468
