Classifications of High Resolution Optical Images using Supervised Algorithms

Abstract

Optical image data have been used by Remote Sensing workforce to study land use and cover, since such data are easily interpretable. The aim of this study is to perform land use classification of optical data using maximum likelihood (ML) and support vector machines (SVM). Essential geo corrections were applied to the images at the pre-processing stage. To appraise the accuracy of the two familiar supervised algorithms, the overall accuracy and kappa coefficient metrics were used. The assessment results demonstrated that the SVM algorithm with an overall accuracy of 88.94% and the kappa-coefficient of 0.87 has a higher accuracy than the ML algorithm. Therefore, the SVM algorithm is suggested to be used as an image classifier for high-resolution optical Remote Sensing images due to its higher accuracy and better reliability.