Oil-Palm Plantation Identification from Satellite Images Using Google Earth Engine

Abstract

Oil-palm plantation is a crucial determinant for land-use planning and agricultural studies. Remote sensing techniques have elevated limitations of the on-site survey as computerized imaging is much efficient and economical. This paper presents a ubiquitous application of Gabor analysis for extracting oil-palm plantation from satellite images. The proposed system was built on the cloud-based Google Earth Engine. Herein, THEOS images were convoluted with Gabor kernels, and both K-Means and SVM then learned their responses for comparison. Experimental results showed that SVM could better identify the plantation areas with precision, recall, and accuracy of 92.98%, 88.96%, and 94.24% respectively