Identification of oil palm plantation in IKONOS images using radially averaged power spectrum values

The use of satellite imagery for plantation management is helpful in monitoring the development of various parties including oil palm plantations. In a panchromatic IKONOS satellite imagery, oil palm plantations have unique characteristics that can be interpreted visually. This study tried to classify oil palm plantations from satellite imagery using texture characteristics with their spatial and frequency parameters. Spatial parameters are determined by calculating the first order features, while the second order texture variables are determined based on Gray Level Co-occurrence Matrix (GLCM), local feature, and Radially Average Power Spectrum Value (RAPSV). The classification accuracy of of this study reached 86%. An addition of average value of the power spectrum has increased the accuracy up to 28% compared to the usage of first order only.