AERIAL IMAGE SEGMENTATION IN URBAN ENVIRONMENT FOR VEGETATION MONITORING
Keywords: SLIC, Aerial Image, Computer Vision, Classifiers, Geoscience, under-sampling , Machine Learning
Abstract. Urban forests are crucial for the population well-being and improvement of the quality of life. For example, they contribute to the rain damping and to the improvement of the local climate. Therefore a correct and accurate mapping of this resource is fundamental for its correct management. We investigated a method that combines machine learning and SLIC superpixel techniques using different Superpixels (k) number to map trees in the metropolitan region of the municipality of Campo Grande-MS, Brazil with aerial orthoimages with GSD (Ground Sample Distance) of 10 cm. The combination of superpixels and machine learning algorithms were checked out with a set of weka classifiers and achieved good results i.e. F-1 %98.2, MCC %88.4 and Accuracy of %96.8, supporting that this method is efficient when used for urban trees mapping.