Multispectral satellite images play a vital role in agriculture, fisheries, and environmental monitoring. However, their high dimensionality, large data volumes, and diverse spatial resolutions across multiple channels pose significant challenges for data compression and analysis. This paper presents ImpliSat, a unified framework specifically designed to address these challenges through efficient compression and reconstruction of multispectral satellite data. ImpliSat leverages Implicit Neural Representations (INR) to model satellite images as continuous functions over coordinate space, capturing fine spatial details across varying spatial resolutions. Furthermore, we introduce a Fourier modulation algorithm that dynamically adjusts to the spectral and spatial characteristics of each band, ensuring optimal compression while preserving critical image details.
@article{arxiv.2506.01234,
title = {Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression},
author = {Woojin Cho and Steve Andreas Immanuel and Junhyuk Heo and Darongsae Kwon},
journal= {arXiv preprint arXiv:2506.01234},
year = {2025}
}