IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA SUPPORT VECTOR MACHINE USING SCIKIT-LEARN MODULE
- 1Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia
- 2Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia
- 3Department of Chemical and Environmental Engineering/Sustainable Process Engineering Research Centre (SPERC), Faculty of Engineering, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- 4Department of Chemical Engineering, University of Bath, Claverton Down, BA2 7AY, United Kingdom
Keywords: Landsat, oil palm, Python, remote sensing, Scikit-learn, support vector machine
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%.