Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images

TL;DR

This paper adapts the HiFiC deep learning model for hyperspectral image compression by introducing two variants that exploit spatio-spectral redundancies, achieving high-quality reconstructions at reduced bitrates.

Contribution

The paper proposes two novel HiFiC-based models, HiFiC$_{SE}$ and HiFiC$_{3D}$, specifically designed for joint spatio-spectral compression of hyperspectral images.

Findings

Effective spatio-spectral compression demonstrated

Higher reconstruction quality at lower bitrates

Models outperform existing methods in preserving image details

Abstract

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 .