Comparison of image classification methods to establishing land cover map at Ha Long city, Quang Ninh province using the Landsat-8 images

Abstract

For a long time, many pixel-based image classification algorithms have been developed for identifying land cover, some of which are commonly used due to their efficiency and accuracy such as Maximum Likelihood (MLC), Support Vector Machines (SVMs), and Decision Trees (DTs). These methods are applied to classify land cover in Ha Long city using Landsat-8 satellite images with several categories including residence, bare soil, forest, agricultural land, water surface, and coal field. The validation results show that the overall accuracy (OA) and Kappa coefficient (K) of these classification methods are high, with OA > 91 % and K > 0.9. However, compared to the other two methods, the DTs method provides the results with the highest accuracy and the best ability to separate features. The obtained results allow selecting the image classification method for identical areas with complicated land cover such as Ha Long.