Classification of land-cover through machine learning algorithms for fusion of sentinel-2A and planetscope imagery

Abstract

To monitor and manage the changes in the land use and land cover, is vital the process of classification; machine learning offers the potential for effective and efficient classification of remotely sensed imagery. However, not many articles have explicitly dealt with the effects of image fusion on land-cover classification accuracy. Although some studies have compared thematic mapping accuracy produced using different classification algorithms, there are no currently many studies that utilize image fusion for assessing different machine learning algorithms for classification purposes. The main aim of this study is to compare different machine learning algorithm for pixel classification of imagery fused with sensors Sentinel-2A and PlanetScope. The method used for image fusion is a variational model, the high spectral resolution of Sentinel-2A imagery and the high spatial resolution of PlanetScope imagery was fused; the machine learning algorithms evaluated are six that have been widely used in the remote sensing community: DT (Decision Tree), Boosted DT, RF (Random Forest), SVM radial base (Support Vector Machine), ANN (Artificial Neural Networks), KNN (k-Nearest Neighbors), for the classification four spectral indices (NDVI, NDMI, NDBI, MSAVI) were included, derived of the image fusion. The results show that the highest accuracy was produced by SVM radial base (OA: 87.8%, Kappa: 87%) respect to the other methods, nevertheless the methods RF, Boosted DT and KNN shown to be very powerful methods for classification of the study area.