Deep learning-based image compression methods have led to high rate-distortion performances compared to traditional codecs. Recently, Generative Adversarial Networks (GANs)-based compression models, e.g., High Fidelity Compression (HiFiC), have attracted great attention in the computer vision community. However, most of these works aim for spatial compression only and do not consider the spatio-spectral redundancies observed in hyperspectral images (HSIs). To address this problem, in this paper, we adapt the HiFiC spatial compression model to perform spatio-spectral compression of HSIs. To this end, 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$_{3D}$). We analyze the effectiveness of HiFiC$_{SE}$ and HiFiC$_{3D}$ in exploiting 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 and reconstruction at reduced bitrates and higher reconstruction quality when compared to JPEG 2000 and the standard HiFiC spatial compression model. The code of the proposed models is publicly available at https://git.tu-berlin.de/rsim/HSI-SSC .

本研究基于深度学习和生成对抗网络的图像压缩方法，提出适用于高光谱图像的 HiFiC 空间-谱域压缩模型，并证明其在降低比特率和提高重建质量方面比传统 JPEG 2000 和标准 HiFiC 模型具有更高的压缩效率。

生成对抗网络用于高光谱图像的时空压缩