Differences of Image Classification Techniques for Land Use and Land Cover Classification

Differences of Image Classification Techniques for Land Use and Land Cover Classification

Abstract— Land use and land cover classification of remotely sensed data is an important research and commonly used in remote sensing application. In this study, the different types of classification techniques were used by using satellite image of some part of Selangor, Malaysia. For this objective < Final Year Projects 2016 > choice, the land use and land cover was classified with Landsat 8 satellite image and ERDAS Imagine software as the image processing packages. From the classification output, the accuracy assessment and kappa statistic were evaluated to get the most accurate classifier. The optimal performance would be identified by validating the classification results with ground truth data. Of classified image, the Maximum Likelihood technique (overall accuracy 82.5%) is the highest and more applicable for satellite image classification compared with Mahalanobis Distance and Minimum Distance. The accurate classification can produce the correct Land Use and Land
Cover map that can be used for many varieties purposes.