Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest

Anggiratih, Endang; Putra, Agfianto Eko

doi:10.22146/ijccs.37461

Open access Jurnal SINTA 2

Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 2 (2019) · pp. 117–126

DOI: 10.22146/ijccs.37461
ISSN: 2460-7258, 1978-1520
Published: 2019-04-30
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolutional Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%.

Keywords

Collaboration network of the authors

9 authors · 10 co-authorship links · 6 across different organisations

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Universitas Gadjah Mada
Universitas Halu Oleo
Indonesia Open University
Lambung Mangkurat University
Telkom University

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

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Co-author	Organisation	Joint articles
Rahmat Ramadhan	Universitas Halu Oleo	1
Ika Purwanti Ningrum	Universitas Halu Oleo	1
Dian Nursantika	Indonesia Open University	1
Wahyono Wahyono	Universitas Gadjah Mada	1
Muhammad Dzulfikar Fauzi	Universitas Gadjah Mada	1
Nuruddin Wiranda	Lambung Mangkurat University	1
Suyanto Suyanto	Telkom University	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest</dc:title>
	<dc:creator>Anggiratih, Endang</dc:creator>
	<dc:creator>Putra, Agfianto Eko</dc:creator>
	<dc:subject xml:lang="en-US">Department of Computer Science and Electronics</dc:subject>
	<dc:subject xml:lang="en-US">features extraction; ships identification; CNN, ZFNet; Random Forest</dc:subject>
	<dc:description xml:lang="en-US">Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolutional Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%.</dc:description>
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	<dc:date>2019-04-30</dc:date>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 2 (2019): April; 117-126</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en-US">Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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