Neural Compression for Multispectral Satellite Images
Abstract
Multispectral satellite images are essential for applications in agriculture, fisheries, and environmental monitoring. However, the high dimensionality, large data volumes, and diverse spatial resolutions across multiple channels present significant challenges for data compression and analysis. In this paper, we introduce ImpliSat, a unified framework specifically designed to address these challenges through efficient compression and reconstruction of multispectral satellite data. ImpliSat employs Implicit Neural Representations (INR) to model satellite images as continuous functions over coordinate space, capturing fine spatial details across varying spatial resolutions. Additionally, we propose a Fourier modulation algorithm that dynamically adjusts to the spectral and spatial characteristics of each channel, ensuring optimal compression while preserving critical image details.