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Cloud Segmentation, Denoising, and Compression Techniques for use on Sentinel-3 Satellite Data | IEEE Conference Publication | IEEE Xplore
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Cloud Segmentation, Denoising, and Compression Techniques for use on Sentinel-3 Satellite Data

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Ming Chi Wong; Yulin Wei; Julian Halloy
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Abstract:

Satellite images of the Earth's surface have a multitude of uses. However., this data comes with some inherent issues. One such issue is cloud coverage. We propose the us...Show More

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Abstract:

Satellite images of the Earth's surface have a multitude of uses. However., this data comes with some inherent issues. One such issue is cloud coverage. We propose the use of image segmentation to identify clouds in images. Another problem is the size of the data., which must be transferred over the satellite's limited connection bandwidth. We propose several compression methods for reducing data size effectively. The data is of varying quality, so the use of noise removal techniques can improve the accuracy and usability of the data.
Published in: TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)
Date of Conference: 01-04 November 2022
Date Added to IEEE Xplore: 20 December 2022
ISBN Information:

ISSN Information:

DOI: 10.1109/TENCON55691.2022.9977982
Conference Location: Hong Kong, Hong Kong

Funding Agency:

References is not available for this document.
Contents

I. Introduction

There are 6 challenges in the 5th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC22). We selected Challenge 6: Sentinel-3 Satellite Datasets: OLCI & SLS TR as our REU proj ect. Sentinel- 3 Satellite is a dataset consisting of red-green-blue (RGB) images generated from the Sentinel-3 satellite via the Ocean and Land Color Instrument (OLCI). Using those images, we explored different kinds of methods to realize image segmentation, denoising, and compression.

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