Sky/Ground Segmentation Using Different Approaches

This work presents a sky and ground segmentation approach in digital images using the supervised Support Vector Machine (SVM) algorithm based on Whiteness and Blueness indexes, Local Binary Patterns, and Extended morphological profile features. The goal is to separate the image contents in two classes, the sky and the ground. The research is divided into two stages: first, the best features selected by monolithic classifiers using the cross-validation technique. The second stage based on combination of classifiers to segment sky and ground: in a first approach, the strategy consists in segmentation process without dividing the image databases into categories. The second approach carries out segmentation a pre-classification of databases into four categories, City, Highway/Road, Sea/ Harbor, and Nature/Mountain. We used two bases of 1200 images each, containing images with different sky and ground contexts. The first approach is generally adopted in the literature. The second approach, little cited in the literature, presents promising and distinct results for both image bases and allows us to see importance of dividing the images into categories, since there is alteration of the ground context, which leads to different results and greater hit rate.