Landsat Image Classification Based on K-Nearest Neighbor

Abstract

Classification is the process of grouping classes and defining a class and determining the relationship between these classes. Landsat imagery with the distribution of residential areas and agricultural areas can be used to process information on the population density of a particular area. In this study, the classification process of residential images, factory images and rice fields images has been carried out with a total of 58 data. KNN was chosen as the classification algorithm considering the data used is quite simple and few. In this study, GLCM is used for feature extraction features, especially regarding image texture patterns. We have implemented values K=1 to k=11. The best accuracy value is obtained at k=1 which is 100%, while k=3, k=5 has obtained an accuracy of 96.15%. k=7 and k=9 can still be tolerated by getting 76.92% while at k=11 it only gets 57.69%.