COMPARISON OF SEVERAL REMOTE SENSING IMAGE CLASSIFICATION METHODS BASED ON ENVI
Keywords: Supervised Classification, Unsupervised Classification, Accuracy Evaluation, Heze City
Abstract. With the development of remote sensing technology and the increasing accuracy of remote sensing images, research on the accuracy of remote sensing classification is becoming more and more important. However, the classification accuracy obtained by different classification algorithms is also different. To this end, this paper selects the maximum likelihood method and the minimum distance method in the traditional supervised classification, the ISODATA method and the k-means algorithm in the unsupervised classification, and uses these four algorithms to classify the Landsat images in the research area of Heze City. The classification results are obtained and the results are evaluated. Then the four algorithms are compared separately, and the advantages and disadvantages of each algorithm are analyzed. The results show that the classification accuracy of the maximum likelihood method in the supervised classification is relatively high, and the classification accuracy is 82.3281%. The ISODATA algorithm in the supervised classification is superior to the K-means algorithm in clustering effect.