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：基于深度学习的压缩模型由于高光谱数据档案的急剧增长，近年来在遥感领域引起了广泛关注。现有的大多数模型仅实现光谱或空间压缩，未能联合考虑高光谱图像中存在的时空-光谱冗余。为解决这一问题，本文聚焦于高保真压缩（HiFiC）模型（该模型已被证明在空间压缩问题上高度有效），并将其扩展至高光谱图像的时空-光谱压缩。具体而言，我们提出了两种新模型：i）使用Squeeze and Excitation（SE）块的HiFiC（简记为HiFiC$_{SE}$）；以及ii）在高光谱图像压缩框架中引入3D卷积的HiFiC（简记为HiFiC$_{3D}$）。我们分别从通道注意力机制和相互依赖性分析角度，评估了HiFiC$_{SE}$和HiFiC$_{3D}$在压缩时空-光谱冗余方面的有效性。实验结果表明，所提模型在实现时空-光谱压缩的同时，能够以更低的比特率重建出更高质量的输出图像。所提模型的代码已在https://git.tu-berlin.de/rsim/HSI-SSC上公开。