Crop Classification of Worldview-2 Time Series using Support Vector Machine (SVM) and Random Forest (RF)

Land cover mapping using high dimensional data is a common task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are often reported in the literature as efficient classifiers for land cover mapping, particularly, in dealing with high-dimensional data. In this research, the possibility of crop classification on time series of Worldview2 images is evaluated in an integrated approach using two most acknowledged supervised learner including random forest (RF) and support vector machine (SVM)