Parameter tuning of unsupervised algorithms to identify oil spills on the sea surface

Rizky Faristyawan; Pramaditya Wicaksono; Sanjiwana Arjasakusuma; Restu Wardani

doi:10.1117/12.3009603

29 January 2024 Parameter tuning of unsupervised algorithms to identify oil spills on the sea surface

Rizky Faristyawan, Pramaditya Wicaksono, Sanjiwana Arjasakusuma, Restu Wardani

Proceedings Volume 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet; 129770Z (2024) https://doi.org/10.1117/12.3009603
Event: 8th Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 2023, Yogyakarta, Indonesia

Abstract

Oil spills frequently occur on the sea surface due to heightened vessel activities. Oil spills can be detected by applying supervised and unsupervised classification methods to satellite images using radar sensors. Supervised classification methods such as visual interpretation are widely used, but the results are very subjective. Conversely, unsupervised methods, while less subjective, necessitate parameter tuning for accurate results. This study's primary goal is to assess the impact of parameter tuning on unsupervised K-Means and Clustering Large Applications (CLARA) algorithms for detecting sea surface oil spills. It can be concluded that the area of identified oil spills is closely related to the iteration parameters and the number of cluster centers. The results of identification using the unsupervised method with these two algorithms will be compared with reference data from Indonesia National Institute of Aeronautics and Space (LAPAN) as the official institution that provides information regarding oil spills pollution on the sea surface in Indonesia. The main conclusion from this study, parameter tuning is highly required before carrying out the process of identifying oil spills on sea level using the unsupervised method especially related to the number of iterations executed, the desired number of cluster centers, and the clustering type of the algorithm used. Using the tuned parameters, the K-Means algorithm is able to identify oil spill areas that are quantitatively close to the reference data area, but the CLARA algorithm is able to provide identification results that have fewer errors in terms of oil spills look-alikes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rizky Faristyawan, Pramaditya Wicaksono, Sanjiwana Arjasakusuma, and Restu Wardani "Parameter tuning of unsupervised algorithms to identify oil spills on the sea surface", Proc. SPIE 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 129770Z (29 January 2024); https://doi.org/10.1117/12.3009603

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Wind speed

Meteorology

Synthetic aperture radar

Image classification

Satellites

Earth observing sensors

Environmental sensing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years