An improved machine learning-based method for unsupervised characterisation for coral reef monitoring in Earth observation time-series data
File(s)
Published version
Author(s)
AlZayer, Zayad
Mason, Philippa
Platt, Robert
John, Cedric M
Type
Journal Article
Abstract
This study presents an innovative approach to automated coral reef monitoring using satellite imagery, addressing challenges in image quality assessment and correction. The method employs Principal Component Analysis (PCA) coupled with clustering for efficient image selection and quality evaluation, followed by a machine learning-based cloud removal technique using an XGBoost model trained to detect land and cloudy pixels over water. The workflow incorporates depth correction using Lyzenga’s algorithm and superpixel analysis, culminating in an unsupervised classification of reef areas using KMeans. Results demonstrate the effectiveness of this approach in producing consistent, interpretable classifications of reef ecosystems across different imaging conditions. This study highlights the potential for scalable, autonomous monitoring of coral reefs, offering valuable insights for conservation efforts and climate change impact assessment in shallow marine environments.
Date Issued
2025-04-01
Date Acceptance
2025-03-13
Citation
Remote Sensing, 2025, 17 (7)
ISSN
2072-4292
Publisher
MDPI AG
Journal / Book Title
Remote Sensing
Volume
17
Issue
7
Copyright Statement
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
License URL
Subjects
ALGORITHM
cloud detection
coral reefs
DEPTH
Environmental Sciences
Environmental Sciences & Ecology
Geology
Geosciences, Multidisciplinary
Imaging Science & Photographic Technology
Life Sciences & Biomedicine
machine learning
marine observation
MISSION
Physical Sciences
Remote Sensing
Science & Technology
Technology
time-series analysis
Publication Status
Published
Article Number
1244
Date Publish Online
2025-04-01