Jurnal
SINTA 2
Analyzing Depthwise Convolution Based Neural Network: Study Case in Ship Detection and Land Cover Classification
Jurnal Ilmu Komputer dan Informasi (JIKI)
ISSN 2502-9274, 2088-7051
Terbit 2019-07-08
Penerbit Faculty of Computer Science - Universitas Indonesia
Bahasa eng
Abstrak
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.
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Sumber metadata: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, pertama di-harvest 26 Okt 2026, terakhir terlihat 1 Nov 2026.
Metadata asli (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>
</oai_dc:dc>
Sitasi dan pengindeksan
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APA 7
Nugroho, K. A., & Windarto, Y. E. (2019). Analyzing Depthwise Convolution Based Neural Network: Study Case in Ship Detection and Land Cover Classification. Jurnal Ilmu Komputer dan Informasi (JIKI), 12(2), 103–112. https://doi.org/10.21609/jiki.v12i2.752
Salin
IEEE
K. A. Nugroho and Y. E. Windarto, “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, pp. 103–112, 2019, doi: 10.21609/jiki.v12i2.752.
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