mlsanityData to 14 Oct 2026

Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression

arXiv 2506.01234 ↗First posted 2 Jun 2025cs.CV cs.AI eess.IVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 2 Jun 2025.

Builds on

13 of 26 references.

References · 13

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1 more
  1. 2025Unveiling the Potential of Superexpressive Networks in Implicit Neural Representationscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machinescited by 0
  2. 2026ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlinkcited by 0