Image classification for mapping oil palm distribution via support vector machine using Scikit-learn module

N. S.N. Shaharum, H. Z.M. Shafri, W. A.W.A. Ghani, S. Samsatli, B. Yusuf, M. M.A. Al-Habshi, H. M. Prince

Research output: Contribution to journalArticle

Abstract

The world has been alarmed with the global warming effects. Global warming has been a distress towards the environment, thus shorten the Earth's lifespan. It is a challenging task to reduce the global warming effects in a short period, knowing that the human population is increasing along with the electricity and energy demand. In order to reduce the effects, renewable energy is presented as an alternative method to produce energy in a way that will not harm the environment. Oil palm is one of the agricultural crops that produces huge amount of biomass which can be processed and used as a renewable energy source. In 2016, Malaysia has reported over 5 million hectares of land were covered by oil palm plantations. Placing Malaysia as the second largest country of oil palm producer in the world has given it an advantage to produce renewable energy source. However, there is a need to monitor the sustainability of oil palm plantations in Malaysia via effective mapping approaches. This study utilised two different platforms (open source and commercial) using a machine learning algorithm namely Support Vector Machine (SVM) to perform oil palm mapping. An open source Python programming-based technique utilising Scikit-learn module was performed to map the oil palm distribution and the result produced had an overall accuracy of 91.39%. To support and validate the efficiency of the Python programming-based image classification, a commercial remote sensing software (ENVI) was used and compared by implementing the same SVM algorithm and the result showed an overall accuracy of 98.21%.

Original languageEnglish
Pages (from-to)139-145
Number of pages7
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume42
Issue number4/W9
DOIs
Publication statusPublished - 26 Oct 2018
Event2018 International Conference on Geomatic and Geospatial Technology: Geospatial and Disaster Risk Management, GGT 2018 - Kuala Lumpur, Malaysia
Duration: 3 Sep 20185 Sep 2018

Keywords

  • Landsat
  • Oil palm
  • Python
  • Remote sensing
  • Scikit-learn
  • Support vector machine

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

Cite this

Image classification for mapping oil palm distribution via support vector machine using Scikit-learn module. / Shaharum, N. S.N.; Shafri, H. Z.M.; Ghani, W. A.W.A.; Samsatli, S.; Yusuf, B.; Al-Habshi, M. M.A.; Prince, H. M.

In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 4/W9, 26.10.2018, p. 139-145.

Research output: Contribution to journalArticle

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