Comparison of five common land cover supervised classification algorithms based on GF-2 and Landsat8 data

Abstract

With the development of remote sensing technology and the differences in remote sensing image classification, it is particularly important to be able to accurately use classification methods to classify images and to compare classification algorithms. In this paper, taking Yangshuo County as the research area, five common supervised classifications, namely support vector machine (SVM), maximum likelihood classification (MLC), neural network (NN), spectral angle mapping (SAM) and spectral information divergence (SID), are used to classify the land cover of remote sensing image data of GF- 2、Landsat8 and its fusion in the same area. The classification results are obtained and compared. Moreover, the overall classification accuracy (OA) and Kappa coefficient are used to evaluate the performance of the image classification algorithm. The results show that both MLC and SVM perform best on these three data sets. For higher spatial resolution GF-2 and fusion data, the OA and Kappa coefficients of both image data classifiers is 10% higher than those of Landsat8 data with higher spectral resolution.