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Sparse Coding-Based Method Comparison For Land-Use Classification

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 10 No. 2 (2017) · pp. 102–107

DOI
10.21609/jiki.v10i2.480
ISSN
2502-9274, 2088-7051
Published
2017-06-30
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Land-use classification utilize high-resolution remote sensing image. The image is utilized for improving the classification problem. Nonetheless, in other side, the problem becomes more challenging cause the image is too complex. We have to represent the image appropriately. On of the common method to deal with it is Bag of Visual Word (BOVW). The method needs a coding process to get the final data interpretation. There are many methods to do coding such as Hard Quantization Coding (HQ), Sparse Coding (SC), and Locality-constrained Linear Coding (LCC). However, that coding methods use a different assumption. Therefore, we have to compare the result of each coding method. The coding method affects classification accuracy. The best coding method will produce the better classification result. Dataset UC Merced consisted 21 classes is used in this research. The experiment result shows that LCC got better performance / accuracy than SC and HQ. LCC method got 86.48 % accuracy. Furthermore, LCC also got the best performance on various number of training data for each class.

Keywords

Collaboration network of the authors

20 authors · 49 co-authorship links · 20 across different organisations

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

  • University of Indonesia
  • The University of Melbourne
  • Pelita Harapan University
  • University of Edinburgh
  • Monash University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad Febrian RachmadiUniversity of Edinburgh2
Agung SantosoUniversity of Indonesia2
Rafli FilanoUniversity of Indonesia2
Nurul HanifahUniversity of Indonesia2
Muhamad SolehUniversity of Indonesia2
Fikrul Arif NadraUniversity of Indonesia1
Heri KurniawanUniversity of Indonesia1
Benny HardjonoPelita Harapan University1
Novian HabibieUniversity of Indonesia1
Machmud Roby AlhamidiUniversity of Indonesia1
Ari WibisonoUniversity of Indonesia1
Dwi PurnomoAffiliation unknown1
Wisnu JatmikoUniversity of Indonesia1
Siti Hadiyan PratiwiUniversity of Indonesia1
Putri ShaniyaUniversity of Indonesia1
Bayu AnggorojatiMonash University1
Haidlir Achmad NaqviUniversity of Indonesia1

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 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Sparse Coding-Based Method Comparison For Land-Use Classification</dc:title>
	<dc:creator>Sri Arsa, Dewa Made</dc:creator>
	<dc:creator>Jati, Grafika</dc:creator>
	<dc:creator>Hilman, M H</dc:creator>
	<dc:subject xml:lang="en-US">Land-use classification</dc:subject>
	<dc:subject xml:lang="en-US">high-resolution remote sensing image</dc:subject>
	<dc:subject xml:lang="en-US">Bag of Visual Word (BOVW)</dc:subject>
	<dc:subject xml:lang="en-US">Sparse Coding (SC)</dc:subject>
	<dc:subject xml:lang="en-US">Hard Quantization Coding (HQ)</dc:subject>
	<dc:description xml:lang="en-US">Land-use classification utilize  high-resolution remote sensing image. The image is utilized for improving the classification problem. Nonetheless, in other side, the problem becomes more challenging cause the image is too complex. We have to represent the image appropriately. On of the common method to deal with it is Bag of Visual Word (BOVW).  The method needs  a coding process to get the final data interpretation. There are many methods to do coding such as Hard Quantization Coding (HQ), Sparse Coding (SC), and Locality-constrained Linear Coding (LCC). However, that coding methods use a different assumption. Therefore, we have to compare the result of each coding method. The coding method affects classification accuracy. The best coding method will produce the better classification result. Dataset UC Merced consisted 21 classes is used in this research. The experiment result shows that LCC got better performance / accuracy than SC and HQ. LCC method got 86.48 % accuracy. Furthermore, LCC also got the best performance on various number of training data for each class.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2017-06-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/480</dc:identifier>
	<dc:identifier>10.21609/jiki.v10i2.480</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 10 No. 2 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 102-107</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v10i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/480/377</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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