Land Cover Classification using Machine Learning Approaches from High Resolution Images

Abstract

Land cover classification has become an interesting research area in the field of remote sensing. Machine learning techniques have shown great success for various application in the domain of land cover classification. This paper focuses on the classification of land covers obtained from high resolution images using two well-known classification methods by integrating with object-based segmentation technique. First, graph-based minimal spanning tree segmentation was applied to segment the original image pixels into objects. The segmented objects were then used to obtained spectral, spatial and texture features which were then combined to form a single high dimensional feature vector. These features were then used to train and test the artificial neural network (ANN) and support vector machine (SVM). The proposed method was evaluated on a dataset consisting of high resolution multi-spectral images with four classes (tea area, other trees, roads and builds, bare land). The experiments showed that ANN was more accuracy as it scored average accuracy of 82.60% while SVM produced 73.66%. Moreover, when postprocessing using majority analysis was applied, the average accuracy improved to 86.18%.