Evaluation of random forest method for agricultural crop classification

Abstract

This study aims to examine the performance of Random Forest (RF) and Maximum Likelihood Classification (MLC) method to crop classification through pixel-based and parcel-based approaches. Analyses are performed on multispectral SPOT 5 image. First, the SPOT 5 image is classified using the classification methods in pixel-based manner. Next, the produced thematic maps are overlaid with the original agricultural parcels and the frequencies of the pixels within the parcels are computed. Then, the majority of the pixels are assigned as class label to the parcels. Results indicate that the overall accuracies of the parcel-based approach computed for the Random Forest method is 85.89%, which is about 8% better than the corresponding result of MLC.