The development of deep learning-based models for the compression of hyperspectral images (HSIs) has recently attracted great attention in remote sensing due to the sharp growing of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression, and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper we focus our attention on the High Fidelity Compression (HiFiC) model (which is proven to be highly effective for spatial compression problems) and adapt it to perform spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiC$_{SE}$); and ii) HiFiC with 3D convolutions (denoted as HiFiC$_{3D}$) in the framework of compression of HSIs. We analyze the effectiveness of HiFiC$_{SE}$ and HiFiC$_{3D}$ in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://git.tu-berlin.de/rsim/HSI-SSC .

翻译：随着高光谱数据档案的急剧增长，基于深度学习的高光谱图像压缩模型开发近年来在遥感领域引起了极大关注。现有模型大多仅实现光谱或空间维度的压缩，未能联合考虑高光谱图像中存在的空谱冗余。针对这一问题，本文聚焦于高保真压缩模型（该模型已被证明在空间压缩问题上极为有效），并将其适配用于高光谱图像的空谱压缩。具体而言，我们引入了两种新模型：i) 采用挤压激励模块的HiFiC（记为HiFiC$_{SE}$）；ii) 在三维卷积框架下实现的HiFiC（记为HiFiC$_{3D}$）。我们通过通道注意力机制与互依赖性分析，研究了HiFiC$_{SE}$与HiFiC$_{3D}$在压缩空谱冗余方面的有效性。实验结果表明，所提模型能够以更低比特率重建图像并获得更高质量的重建结果，有效实现了空谱压缩。模型代码已公开于 https://git.tu-berlin.de/rsim/HSI-SSC。