Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion
Journal:
arXiv
Published Date:
Mar 22, 2025
Abstract
Obtaining high-resolution hyperspectral images (HR-HSI) is costly and
data-intensive, making it necessary to fuse low-resolution hyperspectral images
(LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications.
However, traditional fusion techniques, which integrate detailed information
into the reconstruction, significantly increase bandwidth consumption compared
to directly transmitting raw data. To overcome these challenges, we propose a
hierarchy-aware and channel-adaptive semantic communication approach for
bandwidth-limited data fusion. A hierarchical correlation module is proposed to
preserve both the overall structural information and the details of the image
required for super-resolution. This module efficiently combines deep semantic
and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage
while preserving reconstruction quality, a channel-adaptive attention mechanism
based on Transformer is proposed to dynamically integrate and transmit the deep
and shallow features, enabling efficient data transmission and high-quality
HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall
datasets demonstrate that our method outperforms single-source transmission,
achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR).
Additionally, it reduces bandwidth consumption by two-thirds, confirming its
effectiveness in bandwidth-constrained environments for HR-HSI reconstruction
tasks.
