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Remote Sensing Image Compression Method Based on Implicit Neural Representation

Published: 28 February 2024 Publication History

Abstract

Oceanography-oriented remote sensing image is characterized by being multi-source, heterogeneous, and massive. In recent years, the scale of remote sensing data is exploding, necessitating efficient compression methods for storage. Traditional algorithms have low compression ratio, poor flexibility, and are unsuitable for storing large-scale remote sensing image data. To address this, we propose a remote sensing image lossy compression and storage method based on implicit neural representation. We used an implicit neural network to learn the mapping relationship between longitude, latitude coordinates, and values. Then, we compressed the network weights using quantization. Our method can significantly reduce storage space by approximately 80% for multi-source and heterogeneous remote sensing image data. And the reconstructed quality is capable of meeting the requirements of downstream tasks related to marine AI and visualization.

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ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
October 2023
589 pages
ISBN:9798400707988
DOI:10.1145/3633637
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 28 February 2024

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Author Tags

  1. Compression Techniques
  2. Implicit Neural Network
  3. Remote Sensing

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  • Research-article
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  • Refereed limited

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  • Natural Science Foundation of Shandong Province of China

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ICCPR 2023

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