sparse coding-based method comparison for land-use classification

Clicks: 85
ID: 193118
2017
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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.
Reference Key
arsa2017jurnalsparse Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Dewa Made Sri Arsa;Grafika Jati;M H Hilman
Journal turkish journal of chemistry
Year 2017
DOI
10.21609/jiki.v10i2.480
URL
http://jiki.cs.ui.ac.id/index.php/jiki/article/view/480
Keywords
computer science

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