A paddy growth stages classification using MODIS remote sensing images with balanced branches support vector machines

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6 Citations (Scopus)

Abstract

This paper presents a paddy growth stages classification using MODIS remote sensing images with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired from March to July 2012 along paddy field area only. The data are collected based on growth stages phenology of paddy using spectral profile which consists of at least 9 classes for growth stages and 2 classes for dominated soil and cloud. We apply SVMs to build a binary classifier for each class with one against all strategy of multiclass approach. One important issue needed to address is unbalanced prior probability that should be solved by each SVM. In this study, we evaluate the effectiveness of balanced branches strategy that is applied to one against all SVMs learning. Our results shows that the balanced branches strategy does improves in average around 10% classification accuracy during training and validation, and in average around 50% during testing.

Original languageEnglish
Title of host publication2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
Pages203-206
Number of pages4
Publication statusPublished - 1 Dec 2012
Event2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Depok, Indonesia
Duration: 1 Dec 20122 Dec 2012

Publication series

Name2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings

Conference

Conference2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012
CountryIndonesia
CityDepok
Period1/12/122/12/12

Keywords

  • classification
  • MODIS images
  • Remote sensing
  • support vector machines (SVMs)

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