Land cover classification based optical satellite images using machine learning algorithms

This article aims to apply machine learning algorithms to the supervised classification of optical satellite images. Indeed, the latter is efficient in the study of land use. Despite the performance of machine learning in satellite image processing, this can change but depends on the nature of the satellite images used. Moreover, when we use the satellite, then the reliability of one classifier can be different from the others. In this paper, we examined the performance of DT, SVM, KNN, ANN, and RF. Analysis factors were used to investigate further their importance for Sentinel 2, Landsat 8, Terra Modis, and Spot 5 images. The results show that the KNN showed the most interesting accuracy during the analysis of medium and low-resolution images with spectral bands lower or equal to 4, with a higher accuracy of about 93%. The RF completely dominated the other analysis cases, where the higher accuracy was about 94%. The classification accuracy is more reliable with high-resolution images than with the other resolution categories. However, the processing times of high-resolution images are much higher. Moreover, higher accuracy was often achieved with more expensive processing times. Besides, almost all machine learning algorithms suffered from the Hugs phenomenon during the analyses. So, before the classification with machine learning, some preprocessing is needed.