A Spectral–Spatial Decoupling and Fusion Network with Multi-Scale Perception for Multispectral Image Compression

Abstract

Multispectral images contain rich spatial details and spectral information, but their large data volume places heavy demands on transmission bandwidth and storage resources. Existing multispectral image compression methods still face challenges in jointly modeling spatial and inter-spectral redundancies. In particular, similar feature-processing strategies adopted in some encoder–decoder designs, limited spectral–spatial interaction, and inadequate multi-scale modeling limit further improvements in compression efficiency. To address these issues, this paper proposes a spectral–spatial decoupling and fusion network with multi-scale perception (SSDFN) for multispectral image compression. SSDFN is built on an end-to-end learned compression framework and introduces differentiated spectral- and spatial-oriented processing pathways with tailored fusion mechanisms for the encoder and decoder. The encoder focuses on redundancy suppression and compact latent representation learning, whereas the decoder emphasizes feature interaction, information compensation, and high-fidelity reconstruction. In addition, a Multi-Scale Feature Fusion Block is developed to model complex spatial structures in multispectral remote sensing scenes. Hyperprior and context entropy models are further incorporated to improve entropy estimation. Experiments on the Landsat-8 and Sentinel-2 datasets show that SSDFN achieves competitive rate–distortion performance and provides consistent improvements in spatial reconstruction quality, perceptual quality, and spectral fidelity.