Automatic Environment Classification for Unmanned Aerial Vehicle Using Superpixel Segmentation

Automatic cloud detection has played an important role in meteorological research. However, automatically detecting clouds can also be useful in other fields such as aeronautics, especially for Unmanned Aerial Vehicles (UAVs), since going through dense clouds could destabilize the UAV. Also being aware of the clouds in the surroundings can decreasing the chances of a controlled flight into terrain scenario. This paper shows the design and development of several easy-to-implement superpixel segmentation descriptors with low computational cost, which are robust to incompleteness, geometric distortion, discrimination, and uniqueness. Four of the proposals are developed for cloudsky classification, and a fifth proposal is made for ground-cloudsky classification. Three of the approaches are generated from the extracted histograms of the superpixels obtained from the images. The fourth descriptor, used only for comparison, was obtained by applying SURF to superpixels. Our descriptors proposal is implemented for images obtained from video/photographic cameras mounted on a UAV. Due to its computational cost, it can be computed using low-performance computers. Experimental results showed that when using the proposed descriptors with a Support Vector Machine (SVM) classifier, the obtained recognition rates are improved in comparison with the state-of-the-art feature and texture descriptors used for cloud classification.