Spectral and Spatial Graph Learning for Multispectral Solar Image Compression

• URL: http://arxiv.org/abs/2512.24463v1
• Date: Tue, 30 Dec 2025 20:54:43 GMT
• Title: Spectral and Spatial Graph Learning for Multispectral Solar Image Compression
• Authors: Prasiddha Siwakoti, Atefeh Khoshkhahtinat, Piyush M. Mehta, Barbara J. Thompson, Michael S. F. Kirk, Daniel da Silva,
• Abstract summary: We present a learned image compression framework tailored to solar observations, leveraging two complementary modules.<n> Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15% in Mean Spectral Information Divergence (MSID)<n>A 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates.
• Score: 4.002298833349518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementary modules: (1) the Inter-Spectral Windowed Graph Embedding (iSWGE), which explicitly models inter-band relationships by representing spectral channels as graph nodes with learned edge features; and (2) the Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C), which combines sparse graph attention with convolutional attention to reduce spatial redundancy and emphasize fine-scale structures. Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15%reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates. The code is publicly available at https://github.com/agyat4/sgraph .

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