Hyperspectral Image Compression Using Implicit Neural Representation and Meta-Learned Based Network

Faisal Z. Qureshi, Shima Rezasoltani

2025

Abstract

Hyperspectral images capture the electromagnetic spectrum for each pixel in a scene. These often store hundreds of channels per pixel, providing significantly more information compared to a comparably sized RGB color image. As the cost of obtaining hyperspectral images decreases, there is a need to create effective ways for storing, transferring, and interpreting hyperspectral data. In this paper, we develop a neural compression method for hyperspectral images. Our methodology relies on transforming hyperspectral images into implicit neural representations, specifically neural functions that establish a correspondence between coordinates (such as pixel locations) and features (such as pixel spectra). Instead of explicitly saving the weights of the implicit neural representation, we record modulations that are applied to a base network that has been “meta-learned.” These modulations serve as a compressed coding for the hyperspectral image. We conducted an assessment of our approach using four benchmarks—Indian Pines, Jasper Ridge, Pavia University, and Cuprite—and our findings demonstrate that the suggested method posts significantly faster compression times when comparedto existing schemes for hyperspectral image compression.

Paper Citation

in Harvard Style

Qureshi F. and Rezasoltani S. (2025). Hyperspectral Image Compression Using Implicit Neural Representation and Meta-Learned Based Network. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 23-31. DOI: 10.5220/0013121200003905

in Bibtex Style

@conference{icpram25,
author={Faisal Qureshi and Shima Rezasoltani},
title={Hyperspectral Image Compression Using Implicit Neural Representation and Meta-Learned Based Network},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={23-31},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013121200003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Hyperspectral Image Compression Using Implicit Neural Representation and Meta-Learned Based Network
SN - 978-989-758-730-6
AU - Qureshi F.
AU - Rezasoltani S.
PY - 2025
SP - 23
EP - 31
DO - 10.5220/0013121200003905
PB - SciTePress