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Analyzing Depthwise Convolution Based Neural Network: Study Case in Ship Detection and Land Cover Classification

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 12 No. 2 (2019) · pp. 103–112

DOI
10.21609/jiki.v12i2.752
ISSN
2502-9274, 2088-7051
Published
2019-07-08
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Various methods are available to perform feature extraction on satellite images. Among the available alternatives, deep convolutional neural network (ConvNet) is the state of the art method. Although previous studies have reported successful attempts on developing and implementing ConvNet on remote sensing application, several issues are not well explored, such as the use of depthwise convolution, final pooling layer size, and comparison between grayscale and RGB settings. The objective of this study is to perform analysis to address these issues. Two feature learning algorithms were proposed, namely ConvNet as the current state of the art for satellite image classification and Gray Level Co-occurence Matrix (GLCM) which represents a classic unsupervised feature extraction method. The experiment demonstrated consistent result with previous studies that ConvNet is superior in most cases compared to GLCM, especially with 3x3xn final pooling. The performance of the learning algorithms are much higher on features from RGB channels, except for ConvNet with relatively small number of features.

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Diponegoro University
  • State University of Semarang
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Risma SeptianaDiponegoro University1
Ahmad Fashiha HastawanState University of Semarang1

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://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Analyzing Depthwise Convolution Based Neural Network: Study Case in Ship Detection and Land Cover Classification</dc:title>
	<dc:creator>Nugroho, Kuntoro Adi</dc:creator>
	<dc:creator>Windarto, Yudi Eko</dc:creator>
	<dc:subject xml:lang="en-US">GLCM</dc:subject>
	<dc:subject xml:lang="en-US">Convolutional Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">Satellite Image</dc:subject>
	<dc:description xml:lang="en-US">Various methods are available to perform feature extraction on satellite images. Among the available alternatives, deep convolutional neural network (ConvNet) is the state of the art method. Although previous studies have reported successful attempts on developing and implementing ConvNet on remote sensing application, several issues are not well explored, such as the use of depthwise convolution, final pooling layer size, and comparison between grayscale and RGB settings. The objective of this study is to perform analysis to address these issues. Two feature learning algorithms were proposed, namely ConvNet as the current state of the art for satellite image classification and Gray Level Co-occurence Matrix (GLCM) which represents a classic unsupervised feature extraction method. The experiment demonstrated consistent result with previous studies that ConvNet is superior in most cases compared to GLCM, especially with 3x3xn final pooling. The performance of the learning algorithms are much higher on features from RGB channels, except for ConvNet with relatively small number of features.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2019-07-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/752</dc:identifier>
	<dc:identifier>10.21609/jiki.v12i2.752</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 12 No. 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 103-112</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v12i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/752/417</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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